The growth of Sakhalin fir(Abies sachalinen-sis)seedlings,an important forest tree species in northern Hokkaido,Japan,is significantly affected by competition from surrounding vegetation,especially evergreen dwarf bam...The growth of Sakhalin fir(Abies sachalinen-sis)seedlings,an important forest tree species in northern Hokkaido,Japan,is significantly affected by competition from surrounding vegetation,especially evergreen dwarf bamboo.In this study,we investigated the height and root collar diameter(RCD)growth of Sakhalin fir seedlings under various degrees of cover by deciduous vegetation and evergreen dwarf bamboo.Generalized additive models were used to quantify the effects of canopy cover and forest floor cover on the relative growth rates of these two parameters.The canopy cover of Sakhalin fir seedlings had a nonlin-ear negative effect on both the height growth of seedlings in the subsequent year and the RCD growth in the current year,given the general growth pattern in this species,where height growth ceases in early summer and RCD growth con-tinues until autumn.Height growth declined sharply after the canopy cover rate exceeded 50%,while RCD growth declined rapidly between 0 and 50%canopy cover rate.The forest floor cover had a greater negative impact on RCD growth than on height growth.These results suggested that Sakhalin fir seedlings respond to vegetative competition by prioritizing height growth for light acquisition at the expense of diameter growth and possibly root growth for below-ground competition.The cover of evergreen dwarf bamboo reduced the height growth of fir seedlings significantly more than the cover of deciduous vegetation.This difference is likely due to the timing of light availability.When competing with deciduous vegetation,Sakhalin fir seedlings exposed to light during the post-snow melt and early spring before the development of the deciduous vegetation canopy can photosynthesize more effectively,leading to greater height growth.The results of this study highlighted the importance of vegetation control considering the type of vegetation for successful Sakhalin fir reforestation.Adjusting the intensity and timing of weeding based on the presence and abundance of dwarf bamboo and other competing vegetation could potentially reduce weeding costs and increase biodiversity in reforested areas.展开更多
This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vege...This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.展开更多
There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution i...There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.展开更多
Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on ri...Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.展开更多
In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model ave...In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.展开更多
Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pe...Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pendula Roth） in Norwa are presented. The Norwegian National Forest Inventory （NFI） is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputatio in forest inventories as well as future tree height predictions in growth and yield scenario simulations. Methods： Generalized additive mixed models （gamm） are employed to detect and quantify potentially non-linear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand ag as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests. Additionally the models can be locally calibrated by predicting random effects if measured height-diameter pairs are available. Based on the model selection of non-constraint models, shape constraint additive models （scare） were fit tc incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity. Results： Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. / two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatial correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the height-diameter curve and both effects are positive. Conclusions： In this investigation it is assumed that model effects in additive modelling of height-diameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scare may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.展开更多
We propose a method which uses functional singular component to establish functional additive models. The proposed methodology reduces the curve regression problem to ordinary(i.e., scalar) additive regression problem...We propose a method which uses functional singular component to establish functional additive models. The proposed methodology reduces the curve regression problem to ordinary(i.e., scalar) additive regression problems of the singular components of the predictor process and response process. Consistency of estimators for the nonparametric function and prediction are proved, respectively. A simulation study is conducted to investigate the finite sample performances of the proposed estimators.展开更多
This paper considers partially linear additive models with the number of parameters diverging when some linear cons train ts on the parame trie par t are available.This paper proposes a constrained profile least-squar...This paper considers partially linear additive models with the number of parameters diverging when some linear cons train ts on the parame trie par t are available.This paper proposes a constrained profile least-squares estimation for the parametrie components with the nonparametric functions being estimated by basis function approximations.The consistency and asymptotic normality of the restricted estimator are given under some certain conditions.The authors construct a profile likelihood ratio test statistic to test the validity of the linear constraints on the parametrie components,and demonstrate that it follows asymptotically chi-squared distribution under the null and alternative hypo theses.The finite sample performance of the proposed method is illus trated by simulation studies and a data analysis.展开更多
A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation mode...A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.展开更多
The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environmen...The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.展开更多
Background： Measurements of tree heights and diameters are essential in forest assessment and modelling. Tree heights are used for estimating timber volume, site index and other important variables related to forest ...Background： Measurements of tree heights and diameters are essential in forest assessment and modelling. Tree heights are used for estimating timber volume, site index and other important variables related to forest growth and yield, succession and carbon budget models. However, the diameter at breast height （dbh） can be more accurately obtained and at lower cost, than total tree height. Hence, generalized height-diameter （h-d） models that predict tree height from dbh, age and other covariates are needed. For a more flexible but biologically plausible estimation of covariate effects we use shape constrained generalized additive models as an extension of existing h-d model approaches. We use causal site parameters such as index of aridity to enhance the generality and causality of the models and to enable predictions under projected changeable climatic conditions. Methods： We develop unconstrained generalized additive models （GAM） and shape constrained generalized additive models （SCAM） for investigating the possible effects of tree-specific parameters such as tree age, relative diameter at breast height, and site-specific parameters such as index of aridity and sum of daily mean temperature during vegetation period, on the h-d relationship of forests in Lower Saxony, Germany. Results： Some of the derived effects, e.g. effects of age, index of aridity and sum of daily mean temperature have significantly non-linear pattern. The need for using SCAM results from the fact that some of the model effects show partially implausible patterns especially at the boundaries of data ranges. The derived model predicts monotonically increasing levels of tree height with increasing age and temperature sum and decreasing aridity and social rank of a tree within a stand, The definition of constraints leads only to marginal or minor decline in the model statistics like AIC An observed structured spatial trend in tree height is modelled via 2-dimensional surface fitting. Conclusions： We demonstrate that the SCAM approach allows optimal regression modelling flexibility similar to the standard GAM but with the additional possibility of defining specific constraints for the model effects. The longitudinal character of the model allows for tree height imputation for the current status of forests but also for future tree height prediction.展开更多
Anaerobic ammonium oxidation(anammox)plays a vital role in the global nitrogen cycle by mitigating reactive nitrogen.In recent years,its ecological importance has drawn increasing attention.Despite its widespread occu...Anaerobic ammonium oxidation(anammox)plays a vital role in the global nitrogen cycle by mitigating reactive nitrogen.In recent years,its ecological importance has drawn increasing attention.Despite its widespread occurrence,the distribution and quantitative contribution of anammox to global nitrogen loss remain unclear.We collected 390 reported anammox activity measurements which were obtained using 15N isotope tracing techniques and analyzed anammox rate and environmental factors including soil/sediment and water property using generalized additive models(GAMs).Moreover,based on the division of the anammox activity region,we estimated anammox-driven nitrogen loss across different ecosystems including wetlands and oxygen minimum zones(OMZs)ecosystems.Our findings revealed that soil moisture content was the most significant predictor of anammox activity in wetlands ecosystems.Paddy fields contributed 51%of anammox-driven nitrogen loss(32.0 Tg N/yr),followed by rivers/lakes(29%)and wetlands(20%).Asia emerged as the dominant region for anammoxdriven nitrogen loss(30.7 Tg N/yr),with paddy fields making a substantial contribution.North America was the second-largest contributor(25.4 Tg N/yr),with rivers/lakes being the main sources of nitrogen loss.In OMZs ecosystems,nitrate and dissolved oxygen were key factors influencing anammox rates.OMZs were hotspots for anammox,with peak activity at 300 m depth and nitrogen loss totaling 68.6 Tg N/yr,mostly between 100 and 500 m depths.This study underscores the critical role of anammox in global nitrogen cycling and offers a basis for environmental nitrogen management through predictive anammox modeling.展开更多
Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental cons...Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental consequences,particularly in mountain regions.Here we explore spatio-temporal patterns of rime formation as a proxy for the propensity of individual sites to form rime ice.We present the recent time trends in rime ice occurrence and thickness measured by 23 professional meteorological stations in the Czech Republic in 2002–2023.In an exploratory data analysis,we found high year-to-year variability in rime occurrence and thickness at all sites.According to the annual mean number of hours with rime detected,the stations situated at the highest altitudes are significantly different(higher)from the rest of the sites.The highest rime hour and thickness records by far were observed at the LYSA station in the Beskydy(Beskid)Mts situated at the exposed mountaintop and highly elevated above the surrounding terrain.For advanced statistical modelling of rime thickness,we used two generalised additive models that account for long-term trends(potentially nonlinear),seasonal and daily variability.In an expanded model we further considered the effect of the North Atlantic Oscillation(NAO)index.All the parameters included in the models proved to be statistically significant,although the strength of their effect differed.Factors affecting the rime formation(meteorology and terrain)are strongly site-specific and identification of the significance of individual influencing factors remains a challenging task for our future research.Here,we explore a rare long-term rime record with detailed temporal resolution from multiple uniformly measured sites,which significantly enhances our understanding of rime formation.Additionally,the rime record is from a temperate zone,where rime forms only during a small part of the year.展开更多
Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson...Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.展开更多
This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with gener...This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.展开更多
In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationshi...In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model(GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP(calculated by carbon) ranged from 48.03 to 390.56 mg/(m~2·h),with an annual average of 182.77 mg/(m~2·h). The highest PP was observed in May and the lowest in November.Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation(PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a(Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.展开更多
A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of ...A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of dependent variable and independent variables of the model,the link function of dependent variable and smoothing functional form of each independent variable,estimating process of the link function and smooth functions,and the last model modification.Then,the practical modeling test was carried out based on a large amount of hot rolling process data.An integrated variable was proposed to reflect the effects of different chemical compositions such as carbon,silicon,manganese,nickel,chromium,niobium,etc.The integrated chemical composition,strain,strain rate and rolling temperature were selected as independent variables and the cubic spline as the smooth function for them.The modeling process of deformation resistance was realized by SAS software,and the influence curves of the independent variables on deformation resistance were obtained by local scoring algorithm.Some interesting phenomena were found,for example,there is a critical value of strain rate,and the deformation resistance increases before this value and then decreases.The results confirm that the new model has higher prediction accuracy than traditional ones and is suitable for carbon steel,microalloyed steel,alloyed steel and other steel grades.展开更多
Starting with the Aalen （1989） version of Cox （1972） ＇regression model＇ we show the method for construction of ＂any＂ joint survival function given marginal survival functions. Basically, however, we restrict o...Starting with the Aalen （1989） version of Cox （1972） ＇regression model＇ we show the method for construction of ＂any＂ joint survival function given marginal survival functions. Basically, however, we restrict ourselves to model positive stochastic dependences only with the general assumption that the underlying two marginal random variables are centered on the set of nonnegative real values. With only these assumptions we obtain nice general characterization of bivariate probability distributions that may play similar role as the copula methodology. Examples of reliability and biomedical applications are given.展开更多
This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-sco...This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-score function [12] in a window around each time point. The proposed method can be easily implemented, and the resulting estimators are shown to be consistent and asymptotically normal with easily estimated variances. The simulation studies show that our estimation procedure is reliable and useful.展开更多
The component additive modelling approach is based on summing the results from models already calibrated with pure mineral phases. The summation can occur as the sum of results for thermodynamic surface speciation mod...The component additive modelling approach is based on summing the results from models already calibrated with pure mineral phases. The summation can occur as the sum of results for thermodynamic surface speciation models or as the sum of pseudo-thermodynamic models for adsorption on individual mineral phases. Static batch sorption experiments of 63Ni are with different granitic rocks and component minerals. XRD analyses have been used to calculate the percentage mineralogical composition of the granitic rocks. Sorption data has been modelled using non electrostatic correction models to obtain Rdfor the granitic rocks and mineral. Ra values for the granitic rocks predicted from the component additive model have been compared to experimental values. Results showed that predicted Rd values for granite adamellite, biotite granite and rapakivi granite were identical to the experimentally determined values, whereas, for graphic granite and grey Granite, the predicted and experimentally determined Ra values were much different. The results also showed a greater contribution to the bulk Raby feldspar while quartz showed the least contribution to the Rd.展开更多
基金supported by the Ministry of Agriculture,Forestry,and Fisheries of Japan (25093 C)JSPS KAKENHI (JP23H02262)
文摘The growth of Sakhalin fir(Abies sachalinen-sis)seedlings,an important forest tree species in northern Hokkaido,Japan,is significantly affected by competition from surrounding vegetation,especially evergreen dwarf bamboo.In this study,we investigated the height and root collar diameter(RCD)growth of Sakhalin fir seedlings under various degrees of cover by deciduous vegetation and evergreen dwarf bamboo.Generalized additive models were used to quantify the effects of canopy cover and forest floor cover on the relative growth rates of these two parameters.The canopy cover of Sakhalin fir seedlings had a nonlin-ear negative effect on both the height growth of seedlings in the subsequent year and the RCD growth in the current year,given the general growth pattern in this species,where height growth ceases in early summer and RCD growth con-tinues until autumn.Height growth declined sharply after the canopy cover rate exceeded 50%,while RCD growth declined rapidly between 0 and 50%canopy cover rate.The forest floor cover had a greater negative impact on RCD growth than on height growth.These results suggested that Sakhalin fir seedlings respond to vegetative competition by prioritizing height growth for light acquisition at the expense of diameter growth and possibly root growth for below-ground competition.The cover of evergreen dwarf bamboo reduced the height growth of fir seedlings significantly more than the cover of deciduous vegetation.This difference is likely due to the timing of light availability.When competing with deciduous vegetation,Sakhalin fir seedlings exposed to light during the post-snow melt and early spring before the development of the deciduous vegetation canopy can photosynthesize more effectively,leading to greater height growth.The results of this study highlighted the importance of vegetation control considering the type of vegetation for successful Sakhalin fir reforestation.Adjusting the intensity and timing of weeding based on the presence and abundance of dwarf bamboo and other competing vegetation could potentially reduce weeding costs and increase biodiversity in reforested areas.
基金Under the auspices of National Natural Science Foundation of China(No.41001363)
文摘This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.
基金Under the auspices of the Project of National Natural Science Foundation of China ( No. 41001363)Autonomous Project of State Key Laboratory of Resources and Environmental Information System,Geo-information Tupu Theory and Virtual Geoscience
文摘There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.
基金Supported by the National Natural Science Foundation of China （61273131） 111 Project （B12018）+1 种基金 the Innovation Project of Graduate in Jiangsu Province （CXZZ12_0741） the Fundamental Research Funds for the Central Universities （JUDCF12034）
文摘Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.
基金supported by Youth Academic Innocation Team Construction project of Capital University of Economics and Business under Grant No.QNTD202303supported by the Beijing Outstanding Young Scientist Program under Grant No.JWZQ20240101027the National Natural Science Foundation of China under Grant Nos.12031016,12531012 and 12426308。
文摘In the era of massive data,the study of distributed data is a significant topic.Model averaging can be effectively applied to distributed data by combining information from all machines.For linear models,the model averaging approach has been developed in the context of distributed data.However,further investigation is needed for more complex models.In this paper,the authors propose a distributed optimal model averaging approach based on multivariate additive models,which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree.To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions,the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion.The criterion can be computed by transmitting information between the local machines and the center machine in two steps.The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty,and obtain the convergence rate of the weight vector to the theoretically optimal weights.The results remain novel even for additive models with a single response variable.The numerical examples show that the proposed method yields good performance.
基金supported by the Norwegian Institute of Bioeconomy Research(NIBIO)
文摘Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pendula Roth） in Norwa are presented. The Norwegian National Forest Inventory （NFI） is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputatio in forest inventories as well as future tree height predictions in growth and yield scenario simulations. Methods： Generalized additive mixed models （gamm） are employed to detect and quantify potentially non-linear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand ag as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests. Additionally the models can be locally calibrated by predicting random effects if measured height-diameter pairs are available. Based on the model selection of non-constraint models, shape constraint additive models （scare） were fit tc incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity. Results： Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. / two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatial correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the height-diameter curve and both effects are positive. Conclusions： In this investigation it is assumed that model effects in additive modelling of height-diameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scare may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.
基金supported by National Natural Science Foundation of China (Grant Nos. 11171331, 11561006, 11331011)Program for Creative Research Group of National Natural Science Foundation of China (Grant No. 61621003)+4 种基金a Grant from the Key Lab of Random Complex Structure and Data Science, Chinese Academy of Sciencesthe Natural Science Foundation of Shenzhen UniversityResearch Projects of Colleges and Universities in Guangxi (Grant No. KY2015YB171)Innovation Project of Guangxi Graduate Education (Grant No. JGY2015122)a Grant from the Key Base of Humanities and Social Sciences in Guangxi College
文摘We propose a method which uses functional singular component to establish functional additive models. The proposed methodology reduces the curve regression problem to ordinary(i.e., scalar) additive regression problems of the singular components of the predictor process and response process. Consistency of estimators for the nonparametric function and prediction are proved, respectively. A simulation study is conducted to investigate the finite sample performances of the proposed estimators.
基金supported by the National Natural Science Foundation of China under Grant No.11771250the Natural Science Foundation of Shandong Province under Grant No.ZR2019MA002the Program for Scientific Research Innovation of Graduate Dissertation under Grant No.LWCXB201803
文摘This paper considers partially linear additive models with the number of parameters diverging when some linear cons train ts on the parame trie par t are available.This paper proposes a constrained profile least-squares estimation for the parametrie components with the nonparametric functions being estimated by basis function approximations.The consistency and asymptotic normality of the restricted estimator are given under some certain conditions.The authors construct a profile likelihood ratio test statistic to test the validity of the linear constraints on the parametrie components,and demonstrate that it follows asymptotically chi-squared distribution under the null and alternative hypo theses.The finite sample performance of the proposed method is illus trated by simulation studies and a data analysis.
基金Supported by the National Basic Research and Development (973) Program of China (2010CB428402)China Meteorological Administration Special Public Welfare Research Fund (GYHY200706001)
文摘A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.
基金supported by the Fujian Provincial Science and Technology Program“University-Industry Cooperation Project”(2024Y4015)National Key R&D Plan of Strategic International Scientific and Technological Innovation Cooperation Project(2018YFE0207800).
文摘The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.
文摘Background： Measurements of tree heights and diameters are essential in forest assessment and modelling. Tree heights are used for estimating timber volume, site index and other important variables related to forest growth and yield, succession and carbon budget models. However, the diameter at breast height （dbh） can be more accurately obtained and at lower cost, than total tree height. Hence, generalized height-diameter （h-d） models that predict tree height from dbh, age and other covariates are needed. For a more flexible but biologically plausible estimation of covariate effects we use shape constrained generalized additive models as an extension of existing h-d model approaches. We use causal site parameters such as index of aridity to enhance the generality and causality of the models and to enable predictions under projected changeable climatic conditions. Methods： We develop unconstrained generalized additive models （GAM） and shape constrained generalized additive models （SCAM） for investigating the possible effects of tree-specific parameters such as tree age, relative diameter at breast height, and site-specific parameters such as index of aridity and sum of daily mean temperature during vegetation period, on the h-d relationship of forests in Lower Saxony, Germany. Results： Some of the derived effects, e.g. effects of age, index of aridity and sum of daily mean temperature have significantly non-linear pattern. The need for using SCAM results from the fact that some of the model effects show partially implausible patterns especially at the boundaries of data ranges. The derived model predicts monotonically increasing levels of tree height with increasing age and temperature sum and decreasing aridity and social rank of a tree within a stand, The definition of constraints leads only to marginal or minor decline in the model statistics like AIC An observed structured spatial trend in tree height is modelled via 2-dimensional surface fitting. Conclusions： We demonstrate that the SCAM approach allows optimal regression modelling flexibility similar to the standard GAM but with the additional possibility of defining specific constraints for the model effects. The longitudinal character of the model allows for tree height imputation for the current status of forests but also for future tree height prediction.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0750400)the National Natural Science Foundation of China(Nos.91851204,42177063,and 52370185)+1 种基金the Special project of eco-environmental technology for peak carbon dioxide emissions and carbon neutrality(No.RCEES-TDZ-2021-20)the State Key Joint Laboratory of Environmental Simulation and Pollution Control(Research Center for Eco-environmental Sciences,Chinese Academy of Sciences)(No.24Z01ESPCR).
文摘Anaerobic ammonium oxidation(anammox)plays a vital role in the global nitrogen cycle by mitigating reactive nitrogen.In recent years,its ecological importance has drawn increasing attention.Despite its widespread occurrence,the distribution and quantitative contribution of anammox to global nitrogen loss remain unclear.We collected 390 reported anammox activity measurements which were obtained using 15N isotope tracing techniques and analyzed anammox rate and environmental factors including soil/sediment and water property using generalized additive models(GAMs).Moreover,based on the division of the anammox activity region,we estimated anammox-driven nitrogen loss across different ecosystems including wetlands and oxygen minimum zones(OMZs)ecosystems.Our findings revealed that soil moisture content was the most significant predictor of anammox activity in wetlands ecosystems.Paddy fields contributed 51%of anammox-driven nitrogen loss(32.0 Tg N/yr),followed by rivers/lakes(29%)and wetlands(20%).Asia emerged as the dominant region for anammoxdriven nitrogen loss(30.7 Tg N/yr),with paddy fields making a substantial contribution.North America was the second-largest contributor(25.4 Tg N/yr),with rivers/lakes being the main sources of nitrogen loss.In OMZs ecosystems,nitrate and dissolved oxygen were key factors influencing anammox rates.OMZs were hotspots for anammox,with peak activity at 300 m depth and nitrogen loss totaling 68.6 Tg N/yr,mostly between 100 and 500 m depths.This study underscores the critical role of anammox in global nitrogen cycling and offers a basis for environmental nitrogen management through predictive anammox modeling.
基金financially supported by the Technological Agency of the Czech Republic (TAČR), Joint Grant No SS 02030031 ARAMISby the long-term strategic development financing of the Institute of Computer Science of the Czech Academy of Sciences (RVO 67985807)
文摘Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental consequences,particularly in mountain regions.Here we explore spatio-temporal patterns of rime formation as a proxy for the propensity of individual sites to form rime ice.We present the recent time trends in rime ice occurrence and thickness measured by 23 professional meteorological stations in the Czech Republic in 2002–2023.In an exploratory data analysis,we found high year-to-year variability in rime occurrence and thickness at all sites.According to the annual mean number of hours with rime detected,the stations situated at the highest altitudes are significantly different(higher)from the rest of the sites.The highest rime hour and thickness records by far were observed at the LYSA station in the Beskydy(Beskid)Mts situated at the exposed mountaintop and highly elevated above the surrounding terrain.For advanced statistical modelling of rime thickness,we used two generalised additive models that account for long-term trends(potentially nonlinear),seasonal and daily variability.In an expanded model we further considered the effect of the North Atlantic Oscillation(NAO)index.All the parameters included in the models proved to be statistically significant,although the strength of their effect differed.Factors affecting the rime formation(meteorology and terrain)are strongly site-specific and identification of the significance of individual influencing factors remains a challenging task for our future research.Here,we explore a rare long-term rime record with detailed temporal resolution from multiple uniformly measured sites,which significantly enhances our understanding of rime formation.Additionally,the rime record is from a temperate zone,where rime forms only during a small part of the year.
文摘Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.
基金Project(51774219)supported by the National Natural Science Foundation of China
文摘This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.
基金The National Natural Science Foundation of China under contract No.41506136the Scientific Research Foundation of Third Institute of Oceanography,SOA under contract No.2015005
文摘In this study, the horizontal and vertical distribution of primary production(PP) and its monthly variations were described based on field data collected from the Daya Bay in January–December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model(GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP(calculated by carbon) ranged from 48.03 to 390.56 mg/(m~2·h),with an annual average of 182.77 mg/(m~2·h). The highest PP was observed in May and the lowest in November.Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation(PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a(Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes.
基金supported by National Natural Science Foundation of China (51774219)Science and Technology Research Program of Hubei Ministry of Education(D20161103)Youth Science and technology Program of Wuhan(2016070204010099)
文摘A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of dependent variable and independent variables of the model,the link function of dependent variable and smoothing functional form of each independent variable,estimating process of the link function and smooth functions,and the last model modification.Then,the practical modeling test was carried out based on a large amount of hot rolling process data.An integrated variable was proposed to reflect the effects of different chemical compositions such as carbon,silicon,manganese,nickel,chromium,niobium,etc.The integrated chemical composition,strain,strain rate and rolling temperature were selected as independent variables and the cubic spline as the smooth function for them.The modeling process of deformation resistance was realized by SAS software,and the influence curves of the independent variables on deformation resistance were obtained by local scoring algorithm.Some interesting phenomena were found,for example,there is a critical value of strain rate,and the deformation resistance increases before this value and then decreases.The results confirm that the new model has higher prediction accuracy than traditional ones and is suitable for carbon steel,microalloyed steel,alloyed steel and other steel grades.
文摘Starting with the Aalen （1989） version of Cox （1972） ＇regression model＇ we show the method for construction of ＂any＂ joint survival function given marginal survival functions. Basically, however, we restrict ourselves to model positive stochastic dependences only with the general assumption that the underlying two marginal random variables are centered on the set of nonnegative real values. With only these assumptions we obtain nice general characterization of bivariate probability distributions that may play similar role as the copula methodology. Examples of reliability and biomedical applications are given.
基金supported by the Fundamental Research Funds for the Central Universities (QN0914)
文摘This article discusses regression analysis of failure time under the additive hazards model, when the regression coefficients are time-varying. The regression coefficients are estimated locally based on the pseudo-score function [12] in a window around each time point. The proposed method can be easily implemented, and the resulting estimators are shown to be consistent and asymptotically normal with easily estimated variances. The simulation studies show that our estimation procedure is reliable and useful.
文摘The component additive modelling approach is based on summing the results from models already calibrated with pure mineral phases. The summation can occur as the sum of results for thermodynamic surface speciation models or as the sum of pseudo-thermodynamic models for adsorption on individual mineral phases. Static batch sorption experiments of 63Ni are with different granitic rocks and component minerals. XRD analyses have been used to calculate the percentage mineralogical composition of the granitic rocks. Sorption data has been modelled using non electrostatic correction models to obtain Rdfor the granitic rocks and mineral. Ra values for the granitic rocks predicted from the component additive model have been compared to experimental values. Results showed that predicted Rd values for granite adamellite, biotite granite and rapakivi granite were identical to the experimentally determined values, whereas, for graphic granite and grey Granite, the predicted and experimentally determined Ra values were much different. The results also showed a greater contribution to the bulk Raby feldspar while quartz showed the least contribution to the Rd.
