Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head b...Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head blight(FHB)epidemics of wheat,they explored a functional approach using scalar-on-function regression to model a binary outcome(FHB epidemic or non-epidemic)with respect to weather time series spanning 140 days relative to anthesis.The scalar-on-function models fit the data better than previously described logistic regression models.In this work,given the same dataset and models,we attempt to reproduce the article by Shah et al.using a different approach,boosted regression trees.After fitting,the classification accuracy and model statistics are surprisingly good.展开更多
Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate qua...Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate quantitative analysis of the impact of these factors.This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer(MODIS)SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree(HGBRT)model.The results indicated that the monthly distribution of SCF exhibited a bimodal pattern.The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions.Overall,the SCF showed a decreasing trend during 2000-2021.The decrease in SCF occurred at higher elevations,while an increase was observed at lower elevations.At the annual scale,the SCF showed a downward trend in the western regions affected by westerly(52.84%of the QLM).However,the opposite trend was observed in the eastern regions affected by monsoon(45.73%of the QLM).The SCF displayed broadly similar spatial patterns in autumn and winter,with a significant decrease in the western regions and a slight increase in the central and eastern regions.The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF.Furthermore,compared with meteorological factors,a variation of 46.53%in spring surface runoff can be attributed to changes in spring SCF.At the annual scale,temperature and relative humidity were the most important drivers of SCF change.An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF,with a maximum decrease of 0.22%/a.An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF(about 0.06%/a).The impacts of slope and aspect were found to be minimal.At the seasonal scale,the primary factors impacting SCF change varied.In spring,precipitation and wind speed emerged as the primary drivers.In autumn,precipitation and temperature were identified as the primary drivers.In winter,relative humidity and precipitation were the most important drivers.In contrast to the other seasons,slope exerted the strongest influence on SCF change in summer.This study facilitates a detailed quantitative description of SCF change in the QLM,enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region.展开更多
Understanding the influencing factors of ecosystem services(ESs)and their relationships is essential for sustainable ecosystem management in degraded alpine ecosystems.There is a lack of integrated multi-model approac...Understanding the influencing factors of ecosystem services(ESs)and their relationships is essential for sustainable ecosystem management in degraded alpine ecosystems.There is a lack of integrated multi-model approaches to explore the multidimensional influences on ESs and their relationships in alpine ecosystems.Taking the Daxing'anling forest area,Inner Mongolia(DFAIM)as a case study,this study used the integrated valuation of ecosystem services and trade-offs(InVEST)model to quantify four ESs—soil conservation(SC),water yield(WY),carbon storage(CS),and habitat quality(HQ)—from 2013 to 2018.We adopted root mean square deviation(RMSD)and coupling coordination degree models(CCDM)to analyze their relationships,and integrated three complementary approaches—optimal parameter-based geographical detector model(OPGDM),gradient boosting regression tree model(GBRTM),and quantile regression model(QRM)—to reveal multidimensional influencing factors.Key findings include the following:(1)From 2013 to 2018,WY,SC,and HQ declined while CS increased.WY was primarily influenced by mean annual precipitation(MAP),forest ratio(RF),and soil bulk density(SBD);CS and HQ by RF and population density(PD);and SC by slope(S),RF,and MAP.Mean annual temperature(MAT),gross domestic product(GDP),and road network density(RND)showed increasing negative impacts.(2)Low trade-off intensity(TI<0.15)dominated all ES pairs,with RF,MAP,PD,and normalized difference vegetation index(NDVI)being the dominant factors.The factor interactions primarily showed two-factor enhancement patterns.(3)The average coupling coordination degree(CCD)of the four ESs was low and declined over time,with low-CCD areas becoming increasingly prevalent.RF,S,SBD,and NDVI positively influenced CCD,while PD,MAT,GDP,and RND had increasing negative impacts,with over 62%of the factor interactions exceeding the individual factor effects.In summary,ES supply generally decreased.Local relationships showed moderate coordination,while overall relationships indicated primary dysfunction.Land use and natural factors primarily shaped these ES and their relationships,while climate and socioeconomic changes diminished ES supply and intensified competition.We recommend enhancing the resilience of natural systems rather than replacing them,establishing climate adaptation monitoring systems,and promoting conservation tillage and cross-departmental coordination mechanisms for collaborative ES optimization.These results provide valuable insights into the sustainable management of alpine ecosystems.展开更多
We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empiri...We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.展开更多
Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and mai...Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.展开更多
Background： Tropical dry forests cover less than 13 % of the world＇s tropical forests and their area and biodiversity are declining. In southern Africa, the major threat is increasing population pressure, while drou...Background： Tropical dry forests cover less than 13 % of the world＇s tropical forests and their area and biodiversity are declining. In southern Africa, the major threat is increasing population pressure, while drought caused by climate change is a potential threat in the drier transition zones to shrub land. Monitoring climate change impacts in these transition zones is difficult as there is inadequate information on forest composition to allow disentanglement from other environmental drivers. Methods： This study combined historical and modern forest inventories covering an area of 21,000 km2 in a transition zone in Namibia and Angola to distinguish late succession tree communities, to understand their dependence on site factors, and to detect trends in the forest composition over the last 40 years. Results： The woodlands were dominated by six tree species that represented 84 % of the total basal area and can be referred to as Bdikioea - Pterocarpus woodlands. A boosted regression tree analysis revealed that late succession tree communities are primarily determined by climate and topography. The Schinziophyton rautanenfi and Baikiaea plurijuga communities are common on slightly inclined dune or valley slopes and had the highest basal area （5.5 - 6.2 m^2 ha＆-1）. The Burkea africana - Guibourtia coleosperma and Pterocarpus angolensis - Diafium englerianum communities are typical for the sandy plateaux and have a higher proportion of smaller stems caused by a higher fire frequency. A decrease in overall basal area or a trend of increasing domination by the more drought and cold resilient B. africana community was not confirmed by the historical data, but there were significant decreases in basal area for Ochna pulchra and the valuable fruit tree D. englerianum. Conclusions： The slope communities are more sheltered from fire, frost and drought but are more susceptible to human expansion. The community with the important timber tree P. angolensis can best withstand high fire frequency but shows signs of a higher vulnerability to climate change. Conservation and climate adaptation strategies should include protection of the slope communities through refuges. Follow-up studies are needed on short term dynamics, especially near the edges of the transition zone towards shrub land.展开更多
Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence...Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming.Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.展开更多
The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this...The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.展开更多
In the loose and fractured coal seam with particularly low uniaxial compressive strength(UCS),driving a roadway is extremely difficult as roof falling and wall spalling occur frequently.To address this issue,the jet g...In the loose and fractured coal seam with particularly low uniaxial compressive strength(UCS),driving a roadway is extremely difficult as roof falling and wall spalling occur frequently.To address this issue,the jet grouting(JG)technique(high-pressure grout mixed with coal particles)was first introduced in this study to improve the self-supporting ability of coal mass.To evaluate the strength of the jet-grouted coal-grout composite(JG composite),the UCS evolution patterns were analyzed by preparing 405 specimens combining the influential variables of grout types,curing time,and coal to grout(C/G)ratio.Furthermore,the relationships between UCS and these influencing variables were modeled using ensemble learning methods i.e.gradient boosted regression tree(GBRT)and random forest(RF)with their hyperparameters tuned by the particle swarm optimization(PSO).The results showed that the chemical grout composite has higher short-term strength,while the cement grout composite can achieve more stable strength in the long term.The PSO-GBRT and PSO-RF models can both achieve high prediction accuracy.Also,the variable importance analysis demonstrated that the grout type and curing time should be considered carefully.This study provides a robust intelligent model for predicting UCS of JG composites,which boosts JG design in the field.展开更多
Habitat suitability index(HSI)models have been widely used to analyze the relationship between species abundance and environmental factors,and ultimately inform management of marine species.The response of species abu...Habitat suitability index(HSI)models have been widely used to analyze the relationship between species abundance and environmental factors,and ultimately inform management of marine species.The response of species abundance to each environmental variable is different and habitat requirements may change over life history stages and seasons.Therefore,it is necessary to determine the optimal combination of environmental variables in HSI modelling.In this study,generalized additive models(GAMs)were used to determine which environmental variables to be included in the HSI models.Significant variables were retained and weighted in the HSI model according to their relative contribution(%)to the total deviation explained by the boosted regression tree(BRT).The HSI models were applied to evaluate the habitat suitability of mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent areas in 2011 and 2013–2017.Ontogenetic and seasonal variations in HSI models of mantis shrimp were also examined.Among the four models(non-optimized model,BRT informed HSI model,GAM informed HSI model,and both BRT and GAM informed HSI model),both BRT and GAM informed HSI model showed the best performance.Four environmental variables(bottom temperature,depth,distance offshore and sediment type)were selected in the HSI models for four groups(spring-juvenile,spring-adult,falljuvenile and fall-adult)of mantis shrimp.The distribution of habitat suitability showed similar patterns between juveniles and adults,but obvious seasonal variations were observed.This study suggests that the process of optimizing environmental variables in HSI models improves the performance of HSI models,and this optimization strategy could be extended to other marine organisms to enhance the understanding of the habitat suitability of target species.展开更多
The priming effect(PE)plays a critical role in the control of soil carbon(C)cycling and influences the alteration of soil organic C(SOC)decomposition by fresh C input.However,drivers of PE for the fast and slow SOC po...The priming effect(PE)plays a critical role in the control of soil carbon(C)cycling and influences the alteration of soil organic C(SOC)decomposition by fresh C input.However,drivers of PE for the fast and slow SOC pools remain unclear because of the varying results from individual studies.Using meta-analysis in combination with boosted regression tree(BRT)analysis,we evaluated the relative contribution of multiple drivers of PE with substrate and their patterns across each driver gradient.The results showed that the variability of PE was larger for the fast SOC pool than for the slow SOC pool.Based on the BRT analysis,67%and 34%of the variation in PE were explained for the fast and slow SOC pools,respectively.There were seven determinants of PE for the fast SOC pool,with soil total nitrogen(N)content being the most important,followed by,in a descending order,substrate C:N ratio,soil moisture,soil clay content,soil pH,substrate addition rate,and SOC content.The directions of PE were negative when soil total N content and substrate C:N ratio were below 2 g kg~(-1)and 20,respectively,but the directions changed from negative to positive with increasing levels of this two factors.Soils with optimal water content(50%–70%of the water-holding capacity)or moderately low pH(5–6)were prone to producing a greater PE.For the slow SOC pool,soil p H and soil total N content substantially explained the variation in PE.The magnitude of PE was likely to decrease with increasing soil pH for the slow SOC pool.In addition,the magnitude of PE slightly fluctuated with soil N content for the slow SOC pool.Overall,this meta-analysis provided new insights into the distinctive PEs for different SOC pools and indicated knowledge gaps between PE and its regulating factors for the slow SOC pool.展开更多
Sustainable intensification of cultivated land use(SICLU) and large-scale operations(LSO) are widely acknowledged strategies for enhancing agricultural performance.However,the existing literature has faced challenges ...Sustainable intensification of cultivated land use(SICLU) and large-scale operations(LSO) are widely acknowledged strategies for enhancing agricultural performance.However,the existing literature has faced challenges in precisely defining SICLU and constructing comprehensive indicators,which has hindered the exploration of factors influencing LSO within the SICLU framework.To address this gap,we integrated self-efficacy theory into the design of an index framework for evaluating SICLU.We subsequently employed econometric models to analyze the significant factors that impact LSO.Our findings reveal that SICLU can be divided into four key dimensions:intensive management,efficient output,resource conservation,and ecological environment optimization.Furthermore,it is crucial to incorporate belief-based cognitive factors into the index system,as farmers’ understanding of fertilizer and pesticide application significantly influences their willingness to engage in LSO.Moreover,we identify grain market turnover as the most influential factor in promoting LSO,with single-factor contribution rates reaching 70.9% for cultivated land transfer willingness and 62.5% for the total planting areas.Interestingly,unlike irrigation and agricultural machinery inputs,increased labor inputs correspond to larger planting areas for farmers.This trend may be attributed to reduced labor availability because of rural labor migration,whereas the reduction in irrigation and agricultural input is contingent on innovations in production practices and the transfer of cultivated land management rights.Importantly,SICLU dynamically influences LSO,with each index related to SICLU having an optimal range that fosters LSO.These insights offer valuable guidance for policymakers,emphasizing farmers as their central focus,with the adjustment of input and output factors as a means to achieve LSO as the ultimate goal.In conclusion,we propose research avenues for further enriching the SICLU framework to ensure that it aligns with the specific characteristics of regional agricultural development.展开更多
The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timel...The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.展开更多
Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep...Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers.The stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric emissions.The knowledge oflight hydrocarbon solubility in the aqueous environment is essential for the numerical modelling offlow and transport in the subsurface.Herein,we compiled a database containing 2129experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure.Two machine learning algorithms,namely regression tree(RT)and boosted regression tree(BRT)tuned with a Bayesian optimization algorithm(BO)were employed to determine the solubility of gases.The predictions were compared with the experimental data as well as four well-established thermodynamic models.Our analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic models.The coefficient of determination(R2)between experimental and predicted values is 0.99 and the mean squared error(MSE)is 9.97×10^(-8).The leverage statistical approach further confirmed the validity of the model developed.展开更多
Background Fluvial fish habitat in the Northeastern and Midwestern U.S. is substantially affected by natural landscape factors and anthropogenic stressors, with climate change expected to alter natural influences and ...Background Fluvial fish habitat in the Northeastern and Midwestern U.S. is substantially affected by natural landscape factors and anthropogenic stressors, with climate change expected to alter natural influences and exacerbate stressor effects. To conserve fluvial fish species in the future, it is crucial to understand which fish habitats will be most strongly influenced by changing climate, which species are most sensitive to climate change, and how changes in individual species will affect entire assemblages. To answer these questions, we modeled fluvial fish distributions under projected changes in climate to understand how climate could affect suitability of fish habitat for 55 widely distributed fluvial fishes with differing thermal preferences in the region. Using boosted regression tree models, we predicted distributions of fishes at a stream reach scale using four contemporary climate variables including annual mean air temperature, annual precipitation, and variation in monthly air temperature and precipitation along with seven natural landscape and anthropogenic stressor variables. We then used projected values from eight general circulation models(GCMs) during 2041–2080 to evaluate potential patterns in species richness, turnover, and range shifts under climate change across the study region.Results Most cold-water and cool-water species were projected to lose habitat;however, projected habitat loss also occurred for certain small-bodied warm-water species. The percentage change in species richness of all 55 species across reaches ranged from-40.4 to 33.93%, with regions of major species richness losses occurring across southern portions of the Northeastern coast and southern Midwest regions. Species turnover ranged from 0 to 43.5% with substantial turnover occurring along the Northeastern coast and upper Midwest.Conclusions Temperature and precipitation variation will influence fish species distribution substantially. Our findings provide multiple measures describing patterns of fish community change under climate change to aid management and conservation of stream fishes in the future.展开更多
The spatial pattern of phenology reflects long-term plant adaptation to local environments,yet the drivers of these patterns remain poorly understood.Using satellite data from 2001 to 2018,this study employed the norm...The spatial pattern of phenology reflects long-term plant adaptation to local environments,yet the drivers of these patterns remain poorly understood.Using satellite data from 2001 to 2018,this study employed the normalized difference vegetation index for vegetation structural greenness and solar-induced chlorophyll fluorescence for vegetation functional photosynthesis to analyze spring phenology on the Qinghai-Tibetan Plateau(hereafter,QTP).A machine learning method,Boosted Regression Trees(BRT),was applied to evaluate the contributions of 19 abiotic and biotic factors to the spring phenology.The results showed that both the spring leaf phenology(SOS_(NDVI))and photosynthesis phenology(SOS_(CISF))exhibited a delayed trend decreasing from east to west across the QTP.BRT analysis demonstrated shortwave radiation or/and elevation as key drivers,with higher radiation or elevation associated with more delayed spring phenology spatially,likely due to the constraints of extreme radiation and elevations on spring phenology.Furthermore,we also noted that plants were acclimated to strong radiation to some extent with increasing elevation,namely declined negative effect of radiation/elevation on spring phenology.This acclimation likely enhances plant fitness in the harsh environments of the QTP.Our study provides novel insights into plant phenology on the QTP and highlights the importance of integrating spatial and temporal analysis to improve the localization of phenology models.展开更多
Introduction:Suidae-associated zoonotic viruses represent a significant global public health threat through cross-species transmission events.Current research remains limited to localized outbreak investigations and l...Introduction:Suidae-associated zoonotic viruses represent a significant global public health threat through cross-species transmission events.Current research remains limited to localized outbreak investigations and lacks comprehensive,systematic global analysis.Methods:We collected human-Suidae virus data from the National Center for Biotechnology Information(NCBI)Virus Database,integrating viral characteristics,host information,and environmental and anthropogenic factors.Boosted Regression Trees(BRT)models were employed to evaluate cross-species transmission risk and identify key predictive factors.Results:A total of 43 human-Suidae zoonotic viruses reported durng 1882-2022 were evaluated.The Boosted Regression Trees(BRT)model achieved area under the curve(AUC)values of 0.924(training)and 0.804(testing).Host-human phylogenetic distance and viral genome size emerged as the primary predictors.Porcine circovirus 3(PCV3)demonstrated the highest predicted risk(>0.9).Conclusions:This study establishes a data-driven framework for assessing cross-species transmission risk,supporting early warning systems and targeted prevention strategies.The findings underscore the critical importance of One Health approaches and recommend enhanced surveillance and biosecurity measures for high-risk viruses such as PCV3.展开更多
Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion,especially in Tiaty,Baringo,Kenya.These issues create mixed opportunities for pastoral and agro-pastoral livelihoods.M...Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion,especially in Tiaty,Baringo,Kenya.These issues create mixed opportunities for pastoral and agro-pastoral livelihoods.Machine learn-ing methods for land use and land cover(LULC)classification are vital for monitoring environmental changes.Remote sensing advancements increase the potential for classifying land cover,which requires assessing algorithm ac-curacy and efficiency for fragile environments.This research identifies the best algorithms for LULC monitoring and developing adaptive methods for sensi-tive ecosystems.Landsat-9 imagery from January to April 2023 facilitated land use class identification.Preprocessing in the Google Earth Engine applied spec-tral indices such as the NDVI,NDWI,BSI,and NDBI.Supervised classification uses random forest(RF),support vector machine(SVM),classification and re-gression trees(CARTs),gradient boosting trees(GBTs),and naïve Bayes.An accuracy assessment was used to determine the optimal classifiers for future land use analyses.The evaluation revealed that the RF model achieved 84.4%accuracy with a 0.85 weighted F1 score,indicating its effectiveness for complex LULC data.In contrast,the GBT and CART methods yielded moderate F1 scores(0.77 and 0.68),indicating the presence of overclassification and class imbalance issues.The SVM and naïve Bayes methods were less accurate,ren-dering them unsuitable for LULC tasks.RF is optimal for monitoring and plan-ning land use in dynamic arid areas.Future research should explore hybrid methods and diversify training sites to improve performance.展开更多
Based on the 2-min average wind speed observations at 100 automatic weather stations in Shenzhen from January 2008 to December 2018,this study tries to explore the ways to improve wind interpolation quality over the S...Based on the 2-min average wind speed observations at 100 automatic weather stations in Shenzhen from January 2008 to December 2018,this study tries to explore the ways to improve wind interpolation quality over the Shenzhen region.Three IDW based methods,i.e.,traditional inverse distance weight(IDW),modified inverse distance weight(MIDW),and gradient inverse distance weight(GIDW)are used to interpolate the near surface wind field in Shenzhen.In addition,the gradient boosted regression trees(GBRT)model is used to correct the wind interpolation results based on the three IDW based methods.The results show that among the three methods,GIDW has better interpolation effects than the other two in the case of stratified sampling.The MSE and R2 for the GIDW’s in different months are in the range of 1.096-1.605 m/s and 0.340-0.419,respectively.However,in the case of leave-one-group-out crossvalidation,GIDW has no advantage over the other two methods.For the stratified sampling,GBRT effectively corrects the interpolated results by the three IDW based methods.MSE decreases to the range of 0.778-0.923 m/s,and R2 increases to the range of 0.530-0.671.In the nonstation area,the correction effect of GBRT is still robust,even though the elevation frequency distribution of the non-station area is different from that of the stations’area.The correction performance of GBRT mainly comes from its consideration of the nonlinear relationship between wind speed and the elevation,and the combination of historical and current observation data.展开更多
When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,tra...When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,transportation,or textual information),these geotagged photos could help us in constructing user preference profiles at a high level of detail.Therefore,using these geotagged photos,we built a personalised recommendation system to provide attraction recommendations that match a user’s preferences.Specifically,we retrieved a geotagged photo collection from the public API for Flickr(Flickr.com)and fetched a large amount of other contextual information to rebuild a user’s travel history.We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation(the matching process)and candidate ranking.In the matching process,we used a support vector machine model that was modified for multiclass classification to generate the candidate list.In addition,we used a gradient boosting regression tree to score each candidate and rerank the list.Finally,we evaluated our recommendation results with respect to accuracy and ranking ability.Compared with widely used memory-based methods,our proposed method performs significantly better in the cold-start situation and when mining‘long-tail’data.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12071173 and 12171192)Huaian Key Laboratory for Infectious Diseases Control and Prevention(HAP201704).
文摘Plant epidemics are often associated with weather-related variables.It is difficult to identify weather-related predictors for models predicting plant epidemics.In the article by Shah et al.,to predict Fusarium head blight(FHB)epidemics of wheat,they explored a functional approach using scalar-on-function regression to model a binary outcome(FHB epidemic or non-epidemic)with respect to weather time series spanning 140 days relative to anthesis.The scalar-on-function models fit the data better than previously described logistic regression models.In this work,given the same dataset and models,we attempt to reproduce the article by Shah et al.using a different approach,boosted regression trees.After fitting,the classification accuracy and model statistics are surprisingly good.
基金funded by the Key Research and Development Project for Ecological Civilization Construction in Gansu Province(24YFFA010)the Gansu Province Major Science and Technology Project(22ZD6FA005)+2 种基金the Natural Science Foundation of Gansu Province(24JRRA091)the Shanxi Province Basic Research Program(Free Exploration Category)Youth Project(202403021212316)the Science and Technology Innovation Program for Universities in Shanxi Province(2024L327)。
文摘Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate quantitative analysis of the impact of these factors.This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer(MODIS)SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree(HGBRT)model.The results indicated that the monthly distribution of SCF exhibited a bimodal pattern.The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions.Overall,the SCF showed a decreasing trend during 2000-2021.The decrease in SCF occurred at higher elevations,while an increase was observed at lower elevations.At the annual scale,the SCF showed a downward trend in the western regions affected by westerly(52.84%of the QLM).However,the opposite trend was observed in the eastern regions affected by monsoon(45.73%of the QLM).The SCF displayed broadly similar spatial patterns in autumn and winter,with a significant decrease in the western regions and a slight increase in the central and eastern regions.The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF.Furthermore,compared with meteorological factors,a variation of 46.53%in spring surface runoff can be attributed to changes in spring SCF.At the annual scale,temperature and relative humidity were the most important drivers of SCF change.An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF,with a maximum decrease of 0.22%/a.An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF(about 0.06%/a).The impacts of slope and aspect were found to be minimal.At the seasonal scale,the primary factors impacting SCF change varied.In spring,precipitation and wind speed emerged as the primary drivers.In autumn,precipitation and temperature were identified as the primary drivers.In winter,relative humidity and precipitation were the most important drivers.In contrast to the other seasons,slope exerted the strongest influence on SCF change in summer.This study facilitates a detailed quantitative description of SCF change in the QLM,enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region.
基金funded primarily by the Central Public Welfare Research Institutes Basic Research Business Funds to Support the Administration’s Central Work Project(Grant No.CAFYBB2023ZA003-4)the National Natural Science Foundation of China(Grant Nos.31170593 and 31570633)National Forestry and Grassland Administration Forestry Under the Project“Forestry Major Issues Research”(Grant Nos.500102-1776 and 500102-5110).
文摘Understanding the influencing factors of ecosystem services(ESs)and their relationships is essential for sustainable ecosystem management in degraded alpine ecosystems.There is a lack of integrated multi-model approaches to explore the multidimensional influences on ESs and their relationships in alpine ecosystems.Taking the Daxing'anling forest area,Inner Mongolia(DFAIM)as a case study,this study used the integrated valuation of ecosystem services and trade-offs(InVEST)model to quantify four ESs—soil conservation(SC),water yield(WY),carbon storage(CS),and habitat quality(HQ)—from 2013 to 2018.We adopted root mean square deviation(RMSD)and coupling coordination degree models(CCDM)to analyze their relationships,and integrated three complementary approaches—optimal parameter-based geographical detector model(OPGDM),gradient boosting regression tree model(GBRTM),and quantile regression model(QRM)—to reveal multidimensional influencing factors.Key findings include the following:(1)From 2013 to 2018,WY,SC,and HQ declined while CS increased.WY was primarily influenced by mean annual precipitation(MAP),forest ratio(RF),and soil bulk density(SBD);CS and HQ by RF and population density(PD);and SC by slope(S),RF,and MAP.Mean annual temperature(MAT),gross domestic product(GDP),and road network density(RND)showed increasing negative impacts.(2)Low trade-off intensity(TI<0.15)dominated all ES pairs,with RF,MAP,PD,and normalized difference vegetation index(NDVI)being the dominant factors.The factor interactions primarily showed two-factor enhancement patterns.(3)The average coupling coordination degree(CCD)of the four ESs was low and declined over time,with low-CCD areas becoming increasingly prevalent.RF,S,SBD,and NDVI positively influenced CCD,while PD,MAT,GDP,and RND had increasing negative impacts,with over 62%of the factor interactions exceeding the individual factor effects.In summary,ES supply generally decreased.Local relationships showed moderate coordination,while overall relationships indicated primary dysfunction.Land use and natural factors primarily shaped these ES and their relationships,while climate and socioeconomic changes diminished ES supply and intensified competition.We recommend enhancing the resilience of natural systems rather than replacing them,establishing climate adaptation monitoring systems,and promoting conservation tillage and cross-departmental coordination mechanisms for collaborative ES optimization.These results provide valuable insights into the sustainable management of alpine ecosystems.
基金supported by the Major Research Development Program of China(2016YFC0502704)National Science Foundation of China(31670645,31470578 and 31200363)+4 种基金National Forestry Public Welfare Foundation of China(201304205)Fujian Provincial Department of S&T Project(2013YZ0001-1,2015Y0083,2016Y0083,2016T3037 and 2016T3032)Key Laboratory of Urban Environment and Health of CAS(KLUEH-C-201701)Youth Innovation Promotion Association CAS(2014267)Key Program of the Chinese Academy of Sciences(KFZDSW-324)
文摘We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.
基金Project(2017G006-N)supported by the Project of Science and Technology Research and Development Program of China Railway Corporation。
文摘Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.
基金support of The Future Okavango(TFO)and the SASSCAL projects which were funded by the German Federal Ministry of Education and Research under promotion numbers 01 LL 0912 A and 01 LG1201 M respectivelysupport by the KLIMOS ACROPOLIS research platform(Belgian Development Aid through VLIR/ARES)
文摘Background： Tropical dry forests cover less than 13 % of the world＇s tropical forests and their area and biodiversity are declining. In southern Africa, the major threat is increasing population pressure, while drought caused by climate change is a potential threat in the drier transition zones to shrub land. Monitoring climate change impacts in these transition zones is difficult as there is inadequate information on forest composition to allow disentanglement from other environmental drivers. Methods： This study combined historical and modern forest inventories covering an area of 21,000 km2 in a transition zone in Namibia and Angola to distinguish late succession tree communities, to understand their dependence on site factors, and to detect trends in the forest composition over the last 40 years. Results： The woodlands were dominated by six tree species that represented 84 % of the total basal area and can be referred to as Bdikioea - Pterocarpus woodlands. A boosted regression tree analysis revealed that late succession tree communities are primarily determined by climate and topography. The Schinziophyton rautanenfi and Baikiaea plurijuga communities are common on slightly inclined dune or valley slopes and had the highest basal area （5.5 - 6.2 m^2 ha＆-1）. The Burkea africana - Guibourtia coleosperma and Pterocarpus angolensis - Diafium englerianum communities are typical for the sandy plateaux and have a higher proportion of smaller stems caused by a higher fire frequency. A decrease in overall basal area or a trend of increasing domination by the more drought and cold resilient B. africana community was not confirmed by the historical data, but there were significant decreases in basal area for Ochna pulchra and the valuable fruit tree D. englerianum. Conclusions： The slope communities are more sheltered from fire, frost and drought but are more susceptible to human expansion. The community with the important timber tree P. angolensis can best withstand high fire frequency but shows signs of a higher vulnerability to climate change. Conservation and climate adaptation strategies should include protection of the slope communities through refuges. Follow-up studies are needed on short term dynamics, especially near the edges of the transition zone towards shrub land.
基金supported by the National Natural Science Foundation of China (Nos. 41501229, 41371224, 41130530, and 91325301)the China Postdoctoral Science Foundation (No. 2015M581876)
文摘Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree(CART), random forest(RF), boosted regression trees(BRT), and support vector machine(SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies(measured based on area under the receiver operator characteristic curve score(AUC)) and produced more stable results for the BRT(AUC of 0.921 ± 0.012, mean ± standard deviation) and RF(0.908 ± 0.013) algorithms compared to the CART algorithm(0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value(0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming.Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.
基金the National Natural Science Foundation of China(Nos.51608380 and 51538009)the Key Innovation Team Program of the Innovation Talents Promotion Plan by Ministry of Science and Technology of China(No.2016RA4059)the Specific Consultant Research Project of Shanghai Tunnel Engineering Company Ltd.(No.STEC/KJB/XMGL/0130),China。
文摘The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.
基金financially supported by the Fundamental Research Funds for the Central Universities(2020ZDPY0221)。
文摘In the loose and fractured coal seam with particularly low uniaxial compressive strength(UCS),driving a roadway is extremely difficult as roof falling and wall spalling occur frequently.To address this issue,the jet grouting(JG)technique(high-pressure grout mixed with coal particles)was first introduced in this study to improve the self-supporting ability of coal mass.To evaluate the strength of the jet-grouted coal-grout composite(JG composite),the UCS evolution patterns were analyzed by preparing 405 specimens combining the influential variables of grout types,curing time,and coal to grout(C/G)ratio.Furthermore,the relationships between UCS and these influencing variables were modeled using ensemble learning methods i.e.gradient boosted regression tree(GBRT)and random forest(RF)with their hyperparameters tuned by the particle swarm optimization(PSO).The results showed that the chemical grout composite has higher short-term strength,while the cement grout composite can achieve more stable strength in the long term.The PSO-GBRT and PSO-RF models can both achieve high prediction accuracy.Also,the variable importance analysis demonstrated that the grout type and curing time should be considered carefully.This study provides a robust intelligent model for predicting UCS of JG composites,which boosts JG design in the field.
基金The National Key R&D Program of China under contract No.2017YFE0104400the National Natural Science Foundation of China under contract No.31772852the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)under contract No.2018SDKJ0501-2。
文摘Habitat suitability index(HSI)models have been widely used to analyze the relationship between species abundance and environmental factors,and ultimately inform management of marine species.The response of species abundance to each environmental variable is different and habitat requirements may change over life history stages and seasons.Therefore,it is necessary to determine the optimal combination of environmental variables in HSI modelling.In this study,generalized additive models(GAMs)were used to determine which environmental variables to be included in the HSI models.Significant variables were retained and weighted in the HSI model according to their relative contribution(%)to the total deviation explained by the boosted regression tree(BRT).The HSI models were applied to evaluate the habitat suitability of mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent areas in 2011 and 2013–2017.Ontogenetic and seasonal variations in HSI models of mantis shrimp were also examined.Among the four models(non-optimized model,BRT informed HSI model,GAM informed HSI model,and both BRT and GAM informed HSI model),both BRT and GAM informed HSI model showed the best performance.Four environmental variables(bottom temperature,depth,distance offshore and sediment type)were selected in the HSI models for four groups(spring-juvenile,spring-adult,falljuvenile and fall-adult)of mantis shrimp.The distribution of habitat suitability showed similar patterns between juveniles and adults,but obvious seasonal variations were observed.This study suggests that the process of optimizing environmental variables in HSI models improves the performance of HSI models,and this optimization strategy could be extended to other marine organisms to enhance the understanding of the habitat suitability of target species.
文摘The priming effect(PE)plays a critical role in the control of soil carbon(C)cycling and influences the alteration of soil organic C(SOC)decomposition by fresh C input.However,drivers of PE for the fast and slow SOC pools remain unclear because of the varying results from individual studies.Using meta-analysis in combination with boosted regression tree(BRT)analysis,we evaluated the relative contribution of multiple drivers of PE with substrate and their patterns across each driver gradient.The results showed that the variability of PE was larger for the fast SOC pool than for the slow SOC pool.Based on the BRT analysis,67%and 34%of the variation in PE were explained for the fast and slow SOC pools,respectively.There were seven determinants of PE for the fast SOC pool,with soil total nitrogen(N)content being the most important,followed by,in a descending order,substrate C:N ratio,soil moisture,soil clay content,soil pH,substrate addition rate,and SOC content.The directions of PE were negative when soil total N content and substrate C:N ratio were below 2 g kg~(-1)and 20,respectively,but the directions changed from negative to positive with increasing levels of this two factors.Soils with optimal water content(50%–70%of the water-holding capacity)or moderately low pH(5–6)were prone to producing a greater PE.For the slow SOC pool,soil p H and soil total N content substantially explained the variation in PE.The magnitude of PE was likely to decrease with increasing soil pH for the slow SOC pool.In addition,the magnitude of PE slightly fluctuated with soil N content for the slow SOC pool.Overall,this meta-analysis provided new insights into the distinctive PEs for different SOC pools and indicated knowledge gaps between PE and its regulating factors for the slow SOC pool.
基金Under the auspices of National Natural Science Foundation of China(No.42071226,41671176)Taishan Scholars Youth Expert Support Plan of Shandong Province(No.TSQN202306183)。
文摘Sustainable intensification of cultivated land use(SICLU) and large-scale operations(LSO) are widely acknowledged strategies for enhancing agricultural performance.However,the existing literature has faced challenges in precisely defining SICLU and constructing comprehensive indicators,which has hindered the exploration of factors influencing LSO within the SICLU framework.To address this gap,we integrated self-efficacy theory into the design of an index framework for evaluating SICLU.We subsequently employed econometric models to analyze the significant factors that impact LSO.Our findings reveal that SICLU can be divided into four key dimensions:intensive management,efficient output,resource conservation,and ecological environment optimization.Furthermore,it is crucial to incorporate belief-based cognitive factors into the index system,as farmers’ understanding of fertilizer and pesticide application significantly influences their willingness to engage in LSO.Moreover,we identify grain market turnover as the most influential factor in promoting LSO,with single-factor contribution rates reaching 70.9% for cultivated land transfer willingness and 62.5% for the total planting areas.Interestingly,unlike irrigation and agricultural machinery inputs,increased labor inputs correspond to larger planting areas for farmers.This trend may be attributed to reduced labor availability because of rural labor migration,whereas the reduction in irrigation and agricultural input is contingent on innovations in production practices and the transfer of cultivated land management rights.Importantly,SICLU dynamically influences LSO,with each index related to SICLU having an optimal range that fosters LSO.These insights offer valuable guidance for policymakers,emphasizing farmers as their central focus,with the adjustment of input and output factors as a means to achieve LSO as the ultimate goal.In conclusion,we propose research avenues for further enriching the SICLU framework to ensure that it aligns with the specific characteristics of regional agricultural development.
基金funded by the National Key Research and Development Program of China Strategic International Cooperation in Science and Technology Innovation Program (2018YFE0207800)the National Natural Science Foundation of China (31971483)。
文摘The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.
文摘Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers.The stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric emissions.The knowledge oflight hydrocarbon solubility in the aqueous environment is essential for the numerical modelling offlow and transport in the subsurface.Herein,we compiled a database containing 2129experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure.Two machine learning algorithms,namely regression tree(RT)and boosted regression tree(BRT)tuned with a Bayesian optimization algorithm(BO)were employed to determine the solubility of gases.The predictions were compared with the experimental data as well as four well-established thermodynamic models.Our analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic models.The coefficient of determination(R2)between experimental and predicted values is 0.99 and the mean squared error(MSE)is 9.97×10^(-8).The leverage statistical approach further confirmed the validity of the model developed.
基金funded by the United States Geological Survey Aquatic GAP Project
文摘Background Fluvial fish habitat in the Northeastern and Midwestern U.S. is substantially affected by natural landscape factors and anthropogenic stressors, with climate change expected to alter natural influences and exacerbate stressor effects. To conserve fluvial fish species in the future, it is crucial to understand which fish habitats will be most strongly influenced by changing climate, which species are most sensitive to climate change, and how changes in individual species will affect entire assemblages. To answer these questions, we modeled fluvial fish distributions under projected changes in climate to understand how climate could affect suitability of fish habitat for 55 widely distributed fluvial fishes with differing thermal preferences in the region. Using boosted regression tree models, we predicted distributions of fishes at a stream reach scale using four contemporary climate variables including annual mean air temperature, annual precipitation, and variation in monthly air temperature and precipitation along with seven natural landscape and anthropogenic stressor variables. We then used projected values from eight general circulation models(GCMs) during 2041–2080 to evaluate potential patterns in species richness, turnover, and range shifts under climate change across the study region.Results Most cold-water and cool-water species were projected to lose habitat;however, projected habitat loss also occurred for certain small-bodied warm-water species. The percentage change in species richness of all 55 species across reaches ranged from-40.4 to 33.93%, with regions of major species richness losses occurring across southern portions of the Northeastern coast and southern Midwest regions. Species turnover ranged from 0 to 43.5% with substantial turnover occurring along the Northeastern coast and upper Midwest.Conclusions Temperature and precipitation variation will influence fish species distribution substantially. Our findings provide multiple measures describing patterns of fish community change under climate change to aid management and conservation of stream fishes in the future.
基金Joint Key Research Fund under a cooperative agreement between the National Natural Science Foundation of China and Tibet Autonomous Region(U20A2005)Strategic Priority Research Program(A)of the Chinese Academy of Sciences(XDA26050501).
文摘The spatial pattern of phenology reflects long-term plant adaptation to local environments,yet the drivers of these patterns remain poorly understood.Using satellite data from 2001 to 2018,this study employed the normalized difference vegetation index for vegetation structural greenness and solar-induced chlorophyll fluorescence for vegetation functional photosynthesis to analyze spring phenology on the Qinghai-Tibetan Plateau(hereafter,QTP).A machine learning method,Boosted Regression Trees(BRT),was applied to evaluate the contributions of 19 abiotic and biotic factors to the spring phenology.The results showed that both the spring leaf phenology(SOS_(NDVI))and photosynthesis phenology(SOS_(CISF))exhibited a delayed trend decreasing from east to west across the QTP.BRT analysis demonstrated shortwave radiation or/and elevation as key drivers,with higher radiation or elevation associated with more delayed spring phenology spatially,likely due to the constraints of extreme radiation and elevations on spring phenology.Furthermore,we also noted that plants were acclimated to strong radiation to some extent with increasing elevation,namely declined negative effect of radiation/elevation on spring phenology.This acclimation likely enhances plant fitness in the harsh environments of the QTP.Our study provides novel insights into plant phenology on the QTP and highlights the importance of integrating spatial and temporal analysis to improve the localization of phenology models.
文摘Introduction:Suidae-associated zoonotic viruses represent a significant global public health threat through cross-species transmission events.Current research remains limited to localized outbreak investigations and lacks comprehensive,systematic global analysis.Methods:We collected human-Suidae virus data from the National Center for Biotechnology Information(NCBI)Virus Database,integrating viral characteristics,host information,and environmental and anthropogenic factors.Boosted Regression Trees(BRT)models were employed to evaluate cross-species transmission risk and identify key predictive factors.Results:A total of 43 human-Suidae zoonotic viruses reported durng 1882-2022 were evaluated.The Boosted Regression Trees(BRT)model achieved area under the curve(AUC)values of 0.924(training)and 0.804(testing).Host-human phylogenetic distance and viral genome size emerged as the primary predictors.Porcine circovirus 3(PCV3)demonstrated the highest predicted risk(>0.9).Conclusions:This study establishes a data-driven framework for assessing cross-species transmission risk,supporting early warning systems and targeted prevention strategies.The findings underscore the critical importance of One Health approaches and recommend enhanced surveillance and biosecurity measures for high-risk viruses such as PCV3.
文摘Arid and semiarid regions face challenges such as bushland encroachment and agricultural expansion,especially in Tiaty,Baringo,Kenya.These issues create mixed opportunities for pastoral and agro-pastoral livelihoods.Machine learn-ing methods for land use and land cover(LULC)classification are vital for monitoring environmental changes.Remote sensing advancements increase the potential for classifying land cover,which requires assessing algorithm ac-curacy and efficiency for fragile environments.This research identifies the best algorithms for LULC monitoring and developing adaptive methods for sensi-tive ecosystems.Landsat-9 imagery from January to April 2023 facilitated land use class identification.Preprocessing in the Google Earth Engine applied spec-tral indices such as the NDVI,NDWI,BSI,and NDBI.Supervised classification uses random forest(RF),support vector machine(SVM),classification and re-gression trees(CARTs),gradient boosting trees(GBTs),and naïve Bayes.An accuracy assessment was used to determine the optimal classifiers for future land use analyses.The evaluation revealed that the RF model achieved 84.4%accuracy with a 0.85 weighted F1 score,indicating its effectiveness for complex LULC data.In contrast,the GBT and CART methods yielded moderate F1 scores(0.77 and 0.68),indicating the presence of overclassification and class imbalance issues.The SVM and naïve Bayes methods were less accurate,ren-dering them unsuitable for LULC tasks.RF is optimal for monitoring and plan-ning land use in dynamic arid areas.Future research should explore hybrid methods and diversify training sites to improve performance.
基金supported by the Science and Technology Department of Guangdong Province(No.2019B111101002)the Innovation of Science and Technology Commission of Shenzhen Municipality Ministry(No.JCYJ 20210324101006016).
文摘Based on the 2-min average wind speed observations at 100 automatic weather stations in Shenzhen from January 2008 to December 2018,this study tries to explore the ways to improve wind interpolation quality over the Shenzhen region.Three IDW based methods,i.e.,traditional inverse distance weight(IDW),modified inverse distance weight(MIDW),and gradient inverse distance weight(GIDW)are used to interpolate the near surface wind field in Shenzhen.In addition,the gradient boosted regression trees(GBRT)model is used to correct the wind interpolation results based on the three IDW based methods.The results show that among the three methods,GIDW has better interpolation effects than the other two in the case of stratified sampling.The MSE and R2 for the GIDW’s in different months are in the range of 1.096-1.605 m/s and 0.340-0.419,respectively.However,in the case of leave-one-group-out crossvalidation,GIDW has no advantage over the other two methods.For the stratified sampling,GBRT effectively corrects the interpolated results by the three IDW based methods.MSE decreases to the range of 0.778-0.923 m/s,and R2 increases to the range of 0.530-0.671.In the nonstation area,the correction effect of GBRT is still robust,even though the elevation frequency distribution of the non-station area is different from that of the stations’area.The correction performance of GBRT mainly comes from its consideration of the nonlinear relationship between wind speed and the elevation,and the combination of historical and current observation data.
基金supported by grants from the National Key Research and Development Program of China[grant number 2017YFB0503602]the National Natural Science Foundation of China[grant number 41771425],[grant number 41625003],[grant number 41501162]the Beijing Philosophy and Social Science Foundation[grant number 17JDGLB002].
文摘When travelling,people are accustomed to taking and uploading photos on social media websites,which has led to the accumulation of huge numbers of geotagged photos.Combined with multisource information(e.g.weather,transportation,or textual information),these geotagged photos could help us in constructing user preference profiles at a high level of detail.Therefore,using these geotagged photos,we built a personalised recommendation system to provide attraction recommendations that match a user’s preferences.Specifically,we retrieved a geotagged photo collection from the public API for Flickr(Flickr.com)and fetched a large amount of other contextual information to rebuild a user’s travel history.We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation(the matching process)and candidate ranking.In the matching process,we used a support vector machine model that was modified for multiclass classification to generate the candidate list.In addition,we used a gradient boosting regression tree to score each candidate and rerank the list.Finally,we evaluated our recommendation results with respect to accuracy and ranking ability.Compared with widely used memory-based methods,our proposed method performs significantly better in the cold-start situation and when mining‘long-tail’data.
