This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was...This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.展开更多
This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, ...This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.展开更多
Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displ...Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.展开更多
Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The ...Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1.This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework.The temporal and spatial distribution characteristics of China’s poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been analyzed.Based on the SDGs global indicator framework,this paper first constructed SDG 1 China’s district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model data.Secondly,we establish SDG 1 China’s localized partial least squares estimation model and SDG 1 China’s localized machine learning estimation model.Finally,we analyze and verify the spatiotemporal distribution characteristics of China’s poverty areas and counties and their SDG 1 evaluation values.The results show that SDG 1 China’s district and county localization indicator system proposed in this study and SDG 1 China’s localized partial least squares estimation model can better reflect the poverty level of China’s districts and counties.The estimated model R^(2) is 0.65,which can identify 72.77%of China’s national poverty counties.From 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China’s districts and counties is that the SDG evaluation values gradually increase from western China to eastern China.In addition,the average SDG 1 evaluation value of China’s districts and counties increased by 23%from 2012 to 2018.This paper is oriented to the United Nations SDGs framework,explores the SDG 1 localized evaluation method of China’s districts and counties based on multisource remote sensing data,and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals.展开更多
Urban geography has always been concerned about the influence of human settlements on urban vitality,but few studies reveal the influence of human settlements on urban vitality at a micro-scale.This paper analyzes the...Urban geography has always been concerned about the influence of human settlements on urban vitality,but few studies reveal the influence of human settlements on urban vitality at a micro-scale.This paper analyzes the spatial distribution characteristics of human settlements’quality and urban vitality at the micro-scale using Geodetectors and geographic weighted regression to analyze the relationship between human settlements and urban vitality.The results are shown as follows:there is still a significant development space for human settlements quality in Shahekou District,with obvious spatial dependence characteristics and significant gaps between various systems;the urban vitality of Shahekou District has obvious timeliness,and the urban vitality undergoes significant changes over time,which is related to the human settlements quality.The spatial distribution presents a single core spatial distribution structure with strong relative stability.The spatial distribution of cold and hot spots shows a pattern of“high in the north and low in the south,high in the east and low in the west”,with an increasing trend from southwest to northeast;the reachability of public transport has a significant impact on urban vitality.Its synergy with other variables is the leading force forming the spatial distribution of urban vitality.The environmental system,support system and social system are the significant factors affecting the urban vitality of Shahekou District.展开更多
The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remo...The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remote sensing(RS)and geographic information system(GIS)provides data sources and processing platform for RECC monitoring.This study analyzed and established the evaluation index system of RECC by considering particularity in the karst mountainous area of Southwest China;processed multisource RS data(Sentinel-2,Aster-DEM and Landsat-8)to extract the spatial distributions of nine key indexes by GIS techniques(information classification,overlay analysis and raster calculation);proposed the methods of index integration and fuzzy comprehensive evaluation of the RECC by GIS;and took a typical area,Guangnan County in Yunnan Province of China,as an experimental area to explore the effectiveness of the indexes and methods.The results showed that:(1)The important indexes affecting the RECC of karst mountainous area are water resources,tourism resources,position resources,geographical environment and soil erosion environment.(2)Data on cultivated land,construction land,minerals,transportation,water conservancy,ecosystem services,topography,soil erosion and rocky desertification can be obtained from RS data.GIS techniques integrate the information into the RECC results.The data extraction and processing methods are feasible on evaluating RECC.(3)The RECC of Guangnan County was in the mid-carrying level in 2018.The midcarrying and low-carrying levels were the main types,accounting for more than 80.00%of the total study area.The areas with high carrying capacity were mainly distributed in the northern regions of the northwest-southeast line of the county,and other areas have a low carrying capacity comparatively.The coordination between regional resource-environment status and socioeconomic development is the key to improve RECC.This study explores the evaluation index system of RECC in karst mountainous area and the application of multisource RS data and GIS techniques in the comprehensive evaluation.The methods can be applied in related fields to provide suggestions for data/information extraction and integration,and sustainable development.展开更多
To meet the increasing demand of national spatial database infrastructure construction and application, a concept model of China's coastal zone scientific data platform is established based on the information feat...To meet the increasing demand of national spatial database infrastructure construction and application, a concept model of China's coastal zone scientific data platform is established based on the information feature analysis of a compound dataset, consisting of remote sensing data and conventional data. Based on this concept model, the detailed logical database structure and the storage strategy of remote sensing data and their metadata using ArcSDE are designed. The complicated technology of multisources data combination in this research is crucial to the future coastal zone and offshore database construction and practical running, which will provide intelligent information analysis and technological service for coastal zone and offshore investigation, research, development and management.展开更多
The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and...The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and so on. However,the information is modeled and fused traditionally in particular,name some of the known theories: evidential,fuzzy sets,possibilistic,rough sets or conditional events,etc. For several years,researchers have explored the unification of theories enabling the fusion of multisource information and have finally considered random set theory as a powerful mathematical tool. This paper attempts to overall review the close relationships between random set theory and other theories,and introduce recent research results which present how different types of information can be dealt with in this unified framework. Finally,some possible future directions are discussed.展开更多
Sludge is the by-product of wastewater treatment process. Multisource sludge can be defined as sludge from different sources. Based on the sludge properties of five typical cities in the Yangtze River basin, including...Sludge is the by-product of wastewater treatment process. Multisource sludge can be defined as sludge from different sources. Based on the sludge properties of five typical cities in the Yangtze River basin, including Jiujiang, Wuhu, Lu’an, Zhenjiang and Wuhan, this study investigated and summarized the characteristic variations and distribution differences of multiple indicators and substances from municipal sludge, dredged sludge, and river and lake sediments. The results demonstrated pH of multisource sludge was relatively stable in the neutral range. Organic matter and water content among municipal sludge were high and varied considerably between different wastewater treatment plants. Dredged sludge had an obviously higher sand content and wider particle distribution, which could be considered for graded utilization depending on its size. The nutrients composition of river and lake sediments was usually stable and special, with lower nitrogen and phosphorus content but higher potassium levels. The sources of heavy metals and persistent organic pollutants in multisource sludge were correlated, generally much higher among municipal sludge than dredged sludge and river and lake sediments, which were the most important limitation for final land utilization. Despite various properties of multisource sludge, the final fate and destination have some overall similarities, which need to be supplemented and improved by standards and laws. The study provided a preliminary analysis of suitable technical routes for municipal sludge, dredged sludge, river and lake sediments based on their different characteristics respectively, which was of great significance for multisource sludge co-treatment and disposal in the future of China.展开更多
Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13 A3, TRMM 3 B43,...Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13 A3, TRMM 3 B43, and SRTMDEM, for Yunnan Province, China from 2009 to 2018 to calculate the tropical rainfall condition index(TRCI), vegetation condition index(VCI), temperature condition index(TCI), and elevation factors. Principal component analysis(PCA) and analytic hierarchy process(AHP) were used to construct comprehensive drought monitoring models for Yunnan Province. The reliability of the models was verified, following which the drought situation in Yunnan Province for the past ten years was analysed. The results showed that:(1) The comprehensive drought index(CDI) had a high correlation with the standardized precipitation index, standardized precipitation evapotranspiration index, temperature vegetation dryness index, and CLDAS(China Meteorological Administration land data assimilation system), indicating that the CDI was a strong indicator of drought through meteorological, remote sensing and soil moisture monitoring.(2) The droughts from 2009 to 2018 showed generally consistent spatiotemporal changes. Droughts occurred in most parts of the province, with an average drought frequency of 29% and four droughtprone centres.(3) Monthly drought coverage during 2009 to 2014 exceeded that over 2015 to 2018. January had the largest average drought coverage over the study period(61.92%). Droughts at most stations during the remaining months except for October exhibited a weakening trend(slope > 0). The CDI provides a novel approach for drought monitoring in areas with complex terrain such as Yunnan Province.展开更多
The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of ta...The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.展开更多
Current exploration needs are satisfied by multisource technology,which offers low cost,high efficiency,and high precision.The delay time,which determines the separation effects of the multisource blended data,is one ...Current exploration needs are satisfied by multisource technology,which offers low cost,high efficiency,and high precision.The delay time,which determines the separation effects of the multisource blended data,is one of the most crucial parameters in the acquisition and separation of multisource data.This study uses the deblending method of multisource data based on a periodically varying cosine code and analyses the effects of the two parameters,namely,the period amplitude and period length,used in this method on the separation of the multisource blended data.Meanwhile,the obtained coherence data is used to prove the correlation between the separation of multisource data and the two parameters.Examples of synthetic and field data are adopted to demonstrate that from a qualitative perspective,increasing the amplitude of the periodic code improves the separation effect within a reasonable delay time range.When the period length varies in a suitable range,the secondary noise becomes relatively incoherent,resulting in the separation result with a higher signal-to-noise ratio(SNR).From a quantitative perspective,the significant values(Sig.)of the period amplitude and length on the SNRs are less than 0.05,verifying the correlation between the separation of multisource data and the two parameters.展开更多
The article mainly explores the Hopf bifurcation of a kind of nonlinear system with Gaussian white noise excitation and bounded random parameter.Firstly,the nonlinear system with multisource stochastic fac-tors is red...The article mainly explores the Hopf bifurcation of a kind of nonlinear system with Gaussian white noise excitation and bounded random parameter.Firstly,the nonlinear system with multisource stochastic fac-tors is reduced to an equivalent deterministic nonlinear system by the sequential orthogonal decomposi-tion method and the Karhunen-Loeve(K-L)decomposition theory.Secondly,the critical conditions about the Hopf bifurcation of the equivalent deterministic system are obtained.At the same time the influence of multisource stochastic factors on the Hopf bifurcation for the proposed system is explored.Finally,the theorical results are verified by the numerical simulations.展开更多
Urban areas are particularly vulnerable to surface water flooding in a changing environment.A large number of urban surface water flood models have been developed to derive flood inundations and support risk managemen...Urban areas are particularly vulnerable to surface water flooding in a changing environment.A large number of urban surface water flood models have been developed to derive flood inundations and support risk management.However,unlike fluvial and coastal flooding,urban pluvial flooding is often associated with shallow water and thus the model is difficult to validate with traditional monitoring data.In this study,we first developed a full two-dimensional(2D)hydrodynamic model for simulating surface water floods.We further evaluated the model performance with multisource data from flood incidents,including official reports and social media data.The model was tested in the cities of Baoji and Linyi,China,where two surface water flood events recently occurred and caused considerable losses and casualties.In total,350 localized flooding incidents were obtained for the two cities(220 in Baoji and 130 in Linyi)and 313 reports were retained after data cleaning(202 in Baoji and 111 in Linyi).Over 90%of the reported flood incidents fall in urban areas where water depths are predicted to be higher than 0.15 m.The results demonstrate that the model is able to derive the broad patterns of flood inundation at the city scale.The approach tested here could be applied to other flood-prone cities and future research could include water depth information for more robust model validation.展开更多
Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems.However,the complex structure of the collected multisource data and scarcity of fault samples make it difficult to...Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems.However,the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions.To address this challenge,this paper proposes a novel deep-learning model for multisource data augmentation and small sample fault diagnosis.The raw multisource data are first converted into two-dimensional images using the Gramian Angular Field,and a generator is built to transform random noise into images through transposed convolution operations.Then,two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability.The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator.Furthermore,a global optimization strategy is designed to upgrade parameters in the model.The discriminators and generator compete with each other until Nash equilibrium is achieved.A real-world multistep forging machine is adopted to compare and validate the performance of different methods.The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities.Compared with other state-of-the-art models,the proposed approach has better fault diagnosis accuracy in various scenarios.展开更多
The production of coal-based solid waste(CBSW)from coal mining operations poses a significant threat to the ecological environment in mining regions.This research addresses the proper management of CBSW accumulation b...The production of coal-based solid waste(CBSW)from coal mining operations poses a significant threat to the ecological environment in mining regions.This research addresses the proper management of CBSW accumulation by utilizing coal gangue,fly ash,and gasification slag as primary materials for backfill preparation.This study focused on evaluating the early uniaxial compressive strength and fluidity of the backfill while investigating the changing characteristics of early compressive strength,fluidity,and microstructure during the early age of backfill development.Findings showed that the slurry fluidity significantly decreased as the mass concentration increased,whereas factors such as the aggregate-cement mass ratio,fine aggregate content,and fiber content demonstrated no noticeable impact on slurry fluidity.Notably,the early compressive strength of the backfill decreased significantly with an increase in the aggregate-cement mass ratio;however,increases in the mass concentration and fine aggregate content effectively enhanced the early compressive strength of the backfill,serving as key influencing factors.The inclusion of fiber significantly enhanced the early compressive strength of the backfill,with the optimal fiber concentration determined to be∼0.2wt%.Furthermore,increasing the mass concentration or fine aggregate content alleviated the negative impacts of higher aggregate-cement mass ratios on early compressive strength.However,it must be noted that an elevated fine aggregate content may reduce the reinforcing effects of mass concentration on early compressive strength.This leads to enlarged void structures in the samples,whereas increasing the fine aggregate content reduces the void size and range,thereby improving the early compressive strength of the backfill.展开更多
Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Rec...Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Recent advances in machine learning algorithms,coupled with spaceborne remote sensing technique,offer an unprecedented opportunity to map mangroves at species level with high resolution over large extents.However,a single data source or data type is insufficient to capture the complex features of mangrove species and cannot satisfy the need for fine species classification.Moreover,identifying and selecting effective features derived from integrated multisource data are essential for integrating high-dimensional features for mangrove species discrimination.In this study,we developed a novel framework for mangrove species classification using spectral,texture,and polarization information derived from 3-source spaceborne imagery:WorldView-2(WV-2),OrbitaHyperSpectral(OHS),and Advanced Land Observing Satellite-2(ALOS-2).A total of 151 remote sensing features were first extracted,and 18 schemes were designed.Then,a wrapper method by combining extreme gradient boosting with recursive feature elimination(XGBoost-RFE)was conducted to select the sensitive variables and determine the optical subset size of all features.Finally,an ensemble learning algorithm of XGBoost was applied to classify 6 mangrove species in the Zhanjiang Mangrove National Nature Reserve,China.Our results showed that combining multispectral,hyperspectral,and L-band synthetic aperture radar features yielded the best mangrove species classification results,with an overall accuracy of 94.02%,a quantity disagreement of 4.44%,and an allocation disagreement of 1.54%.In addition,this study demonstrated important application potential of the XGBoost classifier.The proposed framework could provide fine-scale data and conduce to mangroves’conservation and restoration.展开更多
Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moist...Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moisture-driven weather events such as fog,rain,and snow.To address this challenge,we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data(temperature,humidity,and wind speed).The framework employs a convolutional neural network(CNN)branch to extract visibility-related visual features from video imagery sequences,while a parallel artificial neural network(ANN)branch decodes nonlinear relationships among the meteorological factors.Cross-modal feature fusion is achieved through an adaptive weighting layer.To validate the framework,multimodal Backpropagation-VGG(BP-VGG)and Backpropagation-ResNet(BP-ResNet)models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport.The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10%compared to unimodal networks relying solely on imagery.Among the multimodal models,BP-ResNet exhibits the best performance with a mean absolute percentage error(MAPE)of 8.5%.Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually,highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models.The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks,which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements.This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values,enhancing its applicability across industries.However,while this approach boosts accuracy and applicability,its performance in critical low-visibility scenarios remains suboptimal,necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.展开更多
In this paper, we present a wavelength depended ray-tracing algorithm to model the indoor multisource channel impulse response for visible light communication （VLC）. We compare the multipath loss difference between ...In this paper, we present a wavelength depended ray-tracing algorithm to model the indoor multisource channel impulse response for visible light communication （VLC）. We compare the multipath loss difference between multisource and unisource channel. We also analyze the root mean square （RMS） delay spread and average time delay of three typical wavelengths as VLC holds a wide spectrum from 380 nm to 780 nm, the spectral reflectance of walls is wavelength-dependent. And the result shows that the blue light emitting diode （LED） owns a larger communication bandwidth than other wavelengths in the room with plastic walls. Also, the path loss of three different wavelengths is compared.展开更多
Based on station precipitation observations,radar quantitative precipitation estimates(QPE), and radar fusion data during Typhoon Fitow(2013), the influence of multisource precipitation data on multiscale urban typhoo...Based on station precipitation observations,radar quantitative precipitation estimates(QPE), and radar fusion data during Typhoon Fitow(2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China,as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%.(2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details.(3)One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.展开更多
基金supported by the National Natural Science Foundation of China (grant numbers 42293351, and U2468221)。
文摘This paper addresses the accuracy and timeliness limitations of traditional comprehensive prediction methods by proposing an approach of decision-level fusion of multisource data.A risk prediction indicator system was established for water and mud inrush in tunnels by analyzing advanced prediction data for specifi c tunnel segments.Additionally,the indicator weights were determined using the analytic hierarchy process combined with the Huber weighting method.Subsequently,a multisource data decision-layer fusion algorithm was utilized to generate fused imaging results for tunnel water and mud inrush risk predictions.Meanwhile,risk analysis was performed for different tunnel sections to achieve spatial and temporal complementarity within the indicator system and optimize redundant information.Finally,model feasibility was validated using the CZ Project Sejila Mountain Tunnel segment as a case study,yielding favorable risk prediction results and enabling effi cient information fusion and support for construction decision-making.
基金supported by the Royal Golden Jubilee(RGJ)Ph.D.Programme(Grant No.PHD/0079/2561)through the National Research Council of Thailand(NRCT)and Thailand Research Fund(TRF).
文摘This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.
基金supported by the National Natural Science Foundation of China(Grant No.51674169)Department of Education of Hebei Province of China(Grant No.ZD2019140)+1 种基金Natural Science Foundation of Hebei Province of China(Grant No.F2019210243)S&T Program of Hebei(Grant No.22375413D)School of Electrical and Electronics Engineering。
文摘Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.
基金supported by the National Natural Science Foundation of China[grant numbers 41971423 and 31972951]the Natural Science Foundation of Hunan Province[grant numbers 2020JJ3020 and 2020JJ5164]+1 种基金the Science and Technology Planning Project of Hunan Province[grant numbers 2019RS2043 and 2019GK2132]the Postgraduate Scientific Research Innovation Project of Hunan Province[grant number CX20210991].
文摘Poverty has always been a global concern that has restricted human development.The first goal(SDG 1)of the United Nations Sustainable Development Goals(SDGs)is to eliminate all forms of poverty all over the world.The establishment of a scientific and effective localized SDG 1 evaluation and monitoring method is the key to achieving SDG 1.This paper proposes SDG 1 China district and county-level localization evaluation method based on multi-source remote sensing data for the United Nations Sustainable Development Framework.The temporal and spatial distribution characteristics of China’s poverty areas and their SDG 1 evaluation values in 2012,2014,2016,and 2018 have been analyzed.Based on the SDGs global indicator framework,this paper first constructed SDG 1 China’s district and county localization indicator system and then extracted multidimensional feature factors from nighttime light images,land cover data,and digital elevation model data.Secondly,we establish SDG 1 China’s localized partial least squares estimation model and SDG 1 China’s localized machine learning estimation model.Finally,we analyze and verify the spatiotemporal distribution characteristics of China’s poverty areas and counties and their SDG 1 evaluation values.The results show that SDG 1 China’s district and county localization indicator system proposed in this study and SDG 1 China’s localized partial least squares estimation model can better reflect the poverty level of China’s districts and counties.The estimated model R^(2) is 0.65,which can identify 72.77%of China’s national poverty counties.From 2012 to 2018,the spatial distribution pattern of SDG evaluation values in China’s districts and counties is that the SDG evaluation values gradually increase from western China to eastern China.In addition,the average SDG 1 evaluation value of China’s districts and counties increased by 23%from 2012 to 2018.This paper is oriented to the United Nations SDGs framework,explores the SDG 1 localized evaluation method of China’s districts and counties based on multisource remote sensing data,and provides a scientific and rapid regional poverty monitoring and evaluation program for the implementation of the 2030 agenda poverty alleviation goals.
文摘Urban geography has always been concerned about the influence of human settlements on urban vitality,but few studies reveal the influence of human settlements on urban vitality at a micro-scale.This paper analyzes the spatial distribution characteristics of human settlements’quality and urban vitality at the micro-scale using Geodetectors and geographic weighted regression to analyze the relationship between human settlements and urban vitality.The results are shown as follows:there is still a significant development space for human settlements quality in Shahekou District,with obvious spatial dependence characteristics and significant gaps between various systems;the urban vitality of Shahekou District has obvious timeliness,and the urban vitality undergoes significant changes over time,which is related to the human settlements quality.The spatial distribution presents a single core spatial distribution structure with strong relative stability.The spatial distribution of cold and hot spots shows a pattern of“high in the north and low in the south,high in the east and low in the west”,with an increasing trend from southwest to northeast;the reachability of public transport has a significant impact on urban vitality.Its synergy with other variables is the leading force forming the spatial distribution of urban vitality.The environmental system,support system and social system are the significant factors affecting the urban vitality of Shahekou District.
基金the support given by the government and official in Guangnan Countyfunded by[National Natural Science Foundation of China]grant number[41361020,40961031]+3 种基金[Joint Fund of Yunnan Provincial Science and Technology Department and Yunnan University]grant number[2018FY001(-017)][Project of Innovative Talents Cultivation for Graduate Students of Yunnan University]grant number[C176230200][Project of Internationalization and Cultural Inheritance and Innovation of Yunnan University]grant number[C176250202][Science Research Fund of Yunnan Provincial Education Department in 2020:Postgraduate]grant number[2020Y0030]。
文摘The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remote sensing(RS)and geographic information system(GIS)provides data sources and processing platform for RECC monitoring.This study analyzed and established the evaluation index system of RECC by considering particularity in the karst mountainous area of Southwest China;processed multisource RS data(Sentinel-2,Aster-DEM and Landsat-8)to extract the spatial distributions of nine key indexes by GIS techniques(information classification,overlay analysis and raster calculation);proposed the methods of index integration and fuzzy comprehensive evaluation of the RECC by GIS;and took a typical area,Guangnan County in Yunnan Province of China,as an experimental area to explore the effectiveness of the indexes and methods.The results showed that:(1)The important indexes affecting the RECC of karst mountainous area are water resources,tourism resources,position resources,geographical environment and soil erosion environment.(2)Data on cultivated land,construction land,minerals,transportation,water conservancy,ecosystem services,topography,soil erosion and rocky desertification can be obtained from RS data.GIS techniques integrate the information into the RECC results.The data extraction and processing methods are feasible on evaluating RECC.(3)The RECC of Guangnan County was in the mid-carrying level in 2018.The midcarrying and low-carrying levels were the main types,accounting for more than 80.00%of the total study area.The areas with high carrying capacity were mainly distributed in the northern regions of the northwest-southeast line of the county,and other areas have a low carrying capacity comparatively.The coordination between regional resource-environment status and socioeconomic development is the key to improve RECC.This study explores the evaluation index system of RECC in karst mountainous area and the application of multisource RS data and GIS techniques in the comprehensive evaluation.The methods can be applied in related fields to provide suggestions for data/information extraction and integration,and sustainable development.
基金the“863”Marine Monitor of Hitech Research and Development Program of China under contract No.,5 2003AA604040 a, 2002AA639640.
文摘To meet the increasing demand of national spatial database infrastructure construction and application, a concept model of China's coastal zone scientific data platform is established based on the information feature analysis of a compound dataset, consisting of remote sensing data and conventional data. Based on this concept model, the detailed logical database structure and the storage strategy of remote sensing data and their metadata using ArcSDE are designed. The complicated technology of multisources data combination in this research is crucial to the future coastal zone and offshore database construction and practical running, which will provide intelligent information analysis and technological service for coastal zone and offshore investigation, research, development and management.
基金Supported in part by the NSFC (No.60934009,60874105)the ZJNSF (Y1080422, R106745)NCET (08-0345)
文摘The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and so on. However,the information is modeled and fused traditionally in particular,name some of the known theories: evidential,fuzzy sets,possibilistic,rough sets or conditional events,etc. For several years,researchers have explored the unification of theories enabling the fusion of multisource information and have finally considered random set theory as a powerful mathematical tool. This paper attempts to overall review the close relationships between random set theory and other theories,and introduce recent research results which present how different types of information can be dealt with in this unified framework. Finally,some possible future directions are discussed.
基金supported by the Scientific Research Project of China Three Gorges Corporation, China (No. 202003081)the National Key Research and Development Program of China (No. 2020YFC1908702)the National Natural Science Foundation of China (NSFC) (No. 52131002)。
文摘Sludge is the by-product of wastewater treatment process. Multisource sludge can be defined as sludge from different sources. Based on the sludge properties of five typical cities in the Yangtze River basin, including Jiujiang, Wuhu, Lu’an, Zhenjiang and Wuhan, this study investigated and summarized the characteristic variations and distribution differences of multiple indicators and substances from municipal sludge, dredged sludge, and river and lake sediments. The results demonstrated pH of multisource sludge was relatively stable in the neutral range. Organic matter and water content among municipal sludge were high and varied considerably between different wastewater treatment plants. Dredged sludge had an obviously higher sand content and wider particle distribution, which could be considered for graded utilization depending on its size. The nutrients composition of river and lake sediments was usually stable and special, with lower nitrogen and phosphorus content but higher potassium levels. The sources of heavy metals and persistent organic pollutants in multisource sludge were correlated, generally much higher among municipal sludge than dredged sludge and river and lake sediments, which were the most important limitation for final land utilization. Despite various properties of multisource sludge, the final fate and destination have some overall similarities, which need to be supplemented and improved by standards and laws. The study provided a preliminary analysis of suitable technical routes for municipal sludge, dredged sludge, river and lake sediments based on their different characteristics respectively, which was of great significance for multisource sludge co-treatment and disposal in the future of China.
基金This research was funded by the Multigovernment International Science and Technology Innovation Cooperation Key Project of the National Key Research and Development Program of China(Grant No.2018YFE0184300)Erasmus+Capacity Building in Higher Education of the Education,Audiovisual and Culture Executive Agency(EACEA)(Grant No.586037-EPP-1-2017-1-HU-EPPKA2CBHE-JP)+3 种基金the National Natural Science Foundation of China(Grant No.41561048)the Technical Methods and Empirical Study on Ecological Assets Measurement in County Level of Yunnan Province(Grant No.ZDZZD201506)the Young and Middleaged Academic and Technical Leaders Reserve Talents Training Program of Yunnan Province(Grant No.2008PY056)the Program for Innovative Research Team(in Science and Technology)at the University of Yunnan Province,IRTSTYN。
文摘Development of drought monitoring techniques is important for understanding and mitigating droughts and for rational agricultural management. This study used data from multiple sources, including MOD13 A3, TRMM 3 B43, and SRTMDEM, for Yunnan Province, China from 2009 to 2018 to calculate the tropical rainfall condition index(TRCI), vegetation condition index(VCI), temperature condition index(TCI), and elevation factors. Principal component analysis(PCA) and analytic hierarchy process(AHP) were used to construct comprehensive drought monitoring models for Yunnan Province. The reliability of the models was verified, following which the drought situation in Yunnan Province for the past ten years was analysed. The results showed that:(1) The comprehensive drought index(CDI) had a high correlation with the standardized precipitation index, standardized precipitation evapotranspiration index, temperature vegetation dryness index, and CLDAS(China Meteorological Administration land data assimilation system), indicating that the CDI was a strong indicator of drought through meteorological, remote sensing and soil moisture monitoring.(2) The droughts from 2009 to 2018 showed generally consistent spatiotemporal changes. Droughts occurred in most parts of the province, with an average drought frequency of 29% and four droughtprone centres.(3) Monthly drought coverage during 2009 to 2014 exceeded that over 2015 to 2018. January had the largest average drought coverage over the study period(61.92%). Droughts at most stations during the remaining months except for October exhibited a weakening trend(slope > 0). The CDI provides a novel approach for drought monitoring in areas with complex terrain such as Yunnan Province.
基金National Science Foundation of China(Nos.41801388,41901397)。
文摘The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.
基金supported by the National Key Research and Development Program of China(2018YFA0702503)the National Natural Science Foundation of China(41674122).
文摘Current exploration needs are satisfied by multisource technology,which offers low cost,high efficiency,and high precision.The delay time,which determines the separation effects of the multisource blended data,is one of the most crucial parameters in the acquisition and separation of multisource data.This study uses the deblending method of multisource data based on a periodically varying cosine code and analyses the effects of the two parameters,namely,the period amplitude and period length,used in this method on the separation of the multisource blended data.Meanwhile,the obtained coherence data is used to prove the correlation between the separation of multisource data and the two parameters.Examples of synthetic and field data are adopted to demonstrate that from a qualitative perspective,increasing the amplitude of the periodic code improves the separation effect within a reasonable delay time range.When the period length varies in a suitable range,the secondary noise becomes relatively incoherent,resulting in the separation result with a higher signal-to-noise ratio(SNR).From a quantitative perspective,the significant values(Sig.)of the period amplitude and length on the SNRs are less than 0.05,verifying the correlation between the separation of multisource data and the two parameters.
基金This work was supported by the grants from the National Nat-ural Science Foundation of China(No.11772002)Ningxia higher education first-class discipline construction funding project(No.NXYLXK2017B09)+2 种基金Major Special project of North Minzu University(No.ZDZX201902)Open project of The Key Laboratory of In-telligent Information and Big Data Processing of NingXia Province(No.2019KLBD008)Postgraduate Innovation Project of North Minzu University(No.YCX22099).
文摘The article mainly explores the Hopf bifurcation of a kind of nonlinear system with Gaussian white noise excitation and bounded random parameter.Firstly,the nonlinear system with multisource stochastic fac-tors is reduced to an equivalent deterministic nonlinear system by the sequential orthogonal decomposi-tion method and the Karhunen-Loeve(K-L)decomposition theory.Secondly,the critical conditions about the Hopf bifurcation of the equivalent deterministic system are obtained.At the same time the influence of multisource stochastic factors on the Hopf bifurcation for the proposed system is explored.Finally,the theorical results are verified by the numerical simulations.
基金supported by the National Natural Science Foundation of China(Grant Nos:42371076,42271089,42461160294)the Key Project of Shanghai Municipal Education Commission(Grant No:2024AI01006)Shanghai Pilot Program for Basic Research(Grant No:TQ20240209)。
文摘Urban areas are particularly vulnerable to surface water flooding in a changing environment.A large number of urban surface water flood models have been developed to derive flood inundations and support risk management.However,unlike fluvial and coastal flooding,urban pluvial flooding is often associated with shallow water and thus the model is difficult to validate with traditional monitoring data.In this study,we first developed a full two-dimensional(2D)hydrodynamic model for simulating surface water floods.We further evaluated the model performance with multisource data from flood incidents,including official reports and social media data.The model was tested in the cities of Baoji and Linyi,China,where two surface water flood events recently occurred and caused considerable losses and casualties.In total,350 localized flooding incidents were obtained for the two cities(220 in Baoji and 130 in Linyi)and 313 reports were retained after data cleaning(202 in Baoji and 111 in Linyi).Over 90%of the reported flood incidents fall in urban areas where water depths are predicted to be higher than 0.15 m.The results demonstrate that the model is able to derive the broad patterns of flood inundation at the city scale.The approach tested here could be applied to other flood-prone cities and future research could include water depth information for more robust model validation.
基金supported by“the Fundamental Research Funds for the Central Universities,”Grant/Award Number 30923011008.
文摘Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems.However,the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions.To address this challenge,this paper proposes a novel deep-learning model for multisource data augmentation and small sample fault diagnosis.The raw multisource data are first converted into two-dimensional images using the Gramian Angular Field,and a generator is built to transform random noise into images through transposed convolution operations.Then,two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability.The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator.Furthermore,a global optimization strategy is designed to upgrade parameters in the model.The discriminators and generator compete with each other until Nash equilibrium is achieved.A real-world multistep forging machine is adopted to compare and validate the performance of different methods.The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities.Compared with other state-of-the-art models,the proposed approach has better fault diagnosis accuracy in various scenarios.
基金Open Fund of Anhui University of Science and Technology Engineering Laboratory for Safe and Precise Coal Mining(No.ESCMMP202402)China Postdoctoral Science Foundation(Nos.2024M750015,2024T170912,and 2023M733481)+2 种基金Key Laboratory of Xinjiang Coal Resources Green Mining,Ministry of Education(No.KLXGY-KB2418)Anhui University of Science and Technology Talent Introduction Research Fund(No.2023yjrc106)National Natural Science Foundation of China(Nos.42402287 and 52130402).
文摘The production of coal-based solid waste(CBSW)from coal mining operations poses a significant threat to the ecological environment in mining regions.This research addresses the proper management of CBSW accumulation by utilizing coal gangue,fly ash,and gasification slag as primary materials for backfill preparation.This study focused on evaluating the early uniaxial compressive strength and fluidity of the backfill while investigating the changing characteristics of early compressive strength,fluidity,and microstructure during the early age of backfill development.Findings showed that the slurry fluidity significantly decreased as the mass concentration increased,whereas factors such as the aggregate-cement mass ratio,fine aggregate content,and fiber content demonstrated no noticeable impact on slurry fluidity.Notably,the early compressive strength of the backfill decreased significantly with an increase in the aggregate-cement mass ratio;however,increases in the mass concentration and fine aggregate content effectively enhanced the early compressive strength of the backfill,serving as key influencing factors.The inclusion of fiber significantly enhanced the early compressive strength of the backfill,with the optimal fiber concentration determined to be∼0.2wt%.Furthermore,increasing the mass concentration or fine aggregate content alleviated the negative impacts of higher aggregate-cement mass ratios on early compressive strength.However,it must be noted that an elevated fine aggregate content may reduce the reinforcing effects of mass concentration on early compressive strength.This leads to enlarged void structures in the samples,whereas increasing the fine aggregate content reduces the void size and range,thereby improving the early compressive strength of the backfill.
基金National Natural Science Foundation of China(42171379,42222103,42101379,and 42171372)Science and Technology Development Program of Jilin Province,China(20210101396JC)+2 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(2017277 and 2021227)Young Scientist Group Project of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(2022QNXZ03)Shenzhen Science and Technology Program(JCYJ20210324093210029).
文摘Accurately and timely monitoring the spatial distribution and composition of mangrove species is critical for assessing mangroves’health,dynamics,and biodiversity,as well as mangroves’degradation and restoration.Recent advances in machine learning algorithms,coupled with spaceborne remote sensing technique,offer an unprecedented opportunity to map mangroves at species level with high resolution over large extents.However,a single data source or data type is insufficient to capture the complex features of mangrove species and cannot satisfy the need for fine species classification.Moreover,identifying and selecting effective features derived from integrated multisource data are essential for integrating high-dimensional features for mangrove species discrimination.In this study,we developed a novel framework for mangrove species classification using spectral,texture,and polarization information derived from 3-source spaceborne imagery:WorldView-2(WV-2),OrbitaHyperSpectral(OHS),and Advanced Land Observing Satellite-2(ALOS-2).A total of 151 remote sensing features were first extracted,and 18 schemes were designed.Then,a wrapper method by combining extreme gradient boosting with recursive feature elimination(XGBoost-RFE)was conducted to select the sensitive variables and determine the optical subset size of all features.Finally,an ensemble learning algorithm of XGBoost was applied to classify 6 mangrove species in the Zhanjiang Mangrove National Nature Reserve,China.Our results showed that combining multispectral,hyperspectral,and L-band synthetic aperture radar features yielded the best mangrove species classification results,with an overall accuracy of 94.02%,a quantity disagreement of 4.44%,and an allocation disagreement of 1.54%.In addition,this study demonstrated important application potential of the XGBoost classifier.The proposed framework could provide fine-scale data and conduce to mangroves’conservation and restoration.
基金Foundation of Key Laboratory of Smart Earth(KF2023ZD03-02)China Meteorological Administration Innovation development project(CXFZ2025J116)+1 种基金National Natural Science Foundation of China(42205197)Basic Research Fund of CAMS(2022Y023,2022Y025)。
文摘Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions.These phenomena exhibit complex nonlinear dependencies on atmospheric processes,particularly during moisture-driven weather events such as fog,rain,and snow.To address this challenge,we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data(temperature,humidity,and wind speed).The framework employs a convolutional neural network(CNN)branch to extract visibility-related visual features from video imagery sequences,while a parallel artificial neural network(ANN)branch decodes nonlinear relationships among the meteorological factors.Cross-modal feature fusion is achieved through an adaptive weighting layer.To validate the framework,multimodal Backpropagation-VGG(BP-VGG)and Backpropagation-ResNet(BP-ResNet)models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport.The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10%compared to unimodal networks relying solely on imagery.Among the multimodal models,BP-ResNet exhibits the best performance with a mean absolute percentage error(MAPE)of 8.5%.Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually,highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models.The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks,which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements.This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values,enhancing its applicability across industries.However,while this approach boosts accuracy and applicability,its performance in critical low-visibility scenarios remains suboptimal,necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.
文摘In this paper, we present a wavelength depended ray-tracing algorithm to model the indoor multisource channel impulse response for visible light communication （VLC）. We compare the multipath loss difference between multisource and unisource channel. We also analyze the root mean square （RMS） delay spread and average time delay of three typical wavelengths as VLC holds a wide spectrum from 380 nm to 780 nm, the spectral reflectance of walls is wavelength-dependent. And the result shows that the blue light emitting diode （LED） owns a larger communication bandwidth than other wavelengths in the room with plastic walls. Also, the path loss of three different wavelengths is compared.
基金This study was sponsored by the National Natural Science Foundation of China(Grant Nos.41871164,41806046)the Shanghai Sailing Program(Grant No.21YF1456900)+1 种基金the Shanghai Philosophy and Social Science Planning Program(Grant No.2021XRM005)the Fundamental Research Funds for the Central Universities(Grant No.2022ECNU-XWK-XK001).
文摘Based on station precipitation observations,radar quantitative precipitation estimates(QPE), and radar fusion data during Typhoon Fitow(2013), the influence of multisource precipitation data on multiscale urban typhoon pluvial flood modeling is studied. Using Shanghai, China,as the study area, a simplified 2D hydrodynamic model is applied to simulations. Combined with actual flood incidents reported by the public and soil moisture data, we perform multiscale verifications and determine the applicability of three precipitation datasets in the modeling. The results are as follows:(1) At the city scale, although QPE have higher spatial resolution, these estimates are lower than station observations. Radar fusion data have both high accuracy and high spatial resolution. For flood depths above 5 cm, the radar fusion precipitation scenario can improve the matching probability by 6%.(2) At the neighborhood scale, the radar fusion precipitation scenario can effectively mitigate the problems of an uneven spatial distribution of stations and a weak QPE to accurately capture pluvial details.(3)One fixed-point assessment shows that different precipitation data have little influence on the temporal characteristics of the modeling result-all three types of data can accurately reflect flood occurrence times. This work can provide a scientific basis for constructing effective urban pluvial flood monitoring systems.
