Soil depth generally varies in mountainous regions in rather complex ways.Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time,effort and consequently r...Soil depth generally varies in mountainous regions in rather complex ways.Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time,effort and consequently relatively large budget to perform.This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran.For this,one hundred sampling points were selected using randomly stratified methodology,and considering all geomorphic surfaces including summit,shoulder,backslope,footslope and toeslope;and soil depth was actually measured.Eleven primary and secondary topographic attributes were derived from the digital elevation model(DEM) at the study area.The result of multiple linear regression indicated that slope,wetness index,catchment area and sediment transport index,which were included in the model,could explain about 76 % of total variability in soil depth at the selected site.This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale.展开更多
1 Introduction The Paleogene strata(with a depth of more than 2500m)in the Bohai sea is complex(Xu Changgui,2006),the reservoir buried deeply,the reservoir prediction is difficult(LAI Weicheng,XU Changgui,2012),and more
In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more tra...In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more traditional LIDAR height metrics.Here the data fusion method consists of back-projecting LIDAR returns onto original MS images,avoiding co-registration errors.The prediction method is based on nonparametric imputation(the most similar neighbor).Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods.Results show improvements when using combinations of LIDAR and MS compared to using either of them alone.The MS sensor has little explanatory capacity for forest variables dependent on tree height,already well determined from LIDAR alone.However,there is potential for variables dependent on tree diameters and their density.The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity,which are best described from synergies between LIDAR heights and NDVI dispersion.Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes.Their inclusion in the predictor dataset may,however,in a few cases be detrimental to accuracy,and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.展开更多
The user’s age and gender play a vital role within the user portrait.In view of the lack of basic attribute information,such as the age and gender of users,this paper constructs an attribute prediction method based o...The user’s age and gender play a vital role within the user portrait.In view of the lack of basic attribute information,such as the age and gender of users,this paper constructs an attribute prediction method based on stacking multimodel integration.The user’s browsing and clicking history is analyzed to predict the user’s basic attributes.First,LR,RF,XGBoost,and ExtraTree were selected as the base classifiers for the first layer of the stacking framework,and the training results of the first layer were input as new training data into the second layer LightGBM for training.Experiments show that the proposed model can improve the accuracy of prediction results.展开更多
Graph data have extensive applications in various domains,including social networks,biological reaction networks,and molecular structures.Graph classification aims to predict the properties of entire graphs,playing a ...Graph data have extensive applications in various domains,including social networks,biological reaction networks,and molecular structures.Graph classification aims to predict the properties of entire graphs,playing a crucial role in many downstream applications.However,existing graph neural network methods require a large amount of labeled data during the training process.In real-world scenarios,the acquisition of labels is extremely costly,resulting in labeled samples typically accounting for only a small portion of all training data,which limits model performance.Current semi-supervised graph classification methods,such as those based on pseudo-labels and knowledge distillation,still face limitations in effectively utilizing unlabeled graph data and mitigating pseudo-label bias issues.To address these challenges,we propose a Semi-supervised graph Contrastive learning based on Associative Memory network and Pseudo-label Similarity(SCoAMPS).SCoAMPS integrates pseudo-labeling techniques with contrastive learning by generating contrastive views through multiple encoders,selecting positive and negative samples using pseudo-label similarity,and defining associative memory network to alleviate pseudo-label bias problems.Experimental results demonstrate that SCoAMPS achieves significant performance improvements on multiple public datasets.展开更多
文摘Soil depth generally varies in mountainous regions in rather complex ways.Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time,effort and consequently relatively large budget to perform.This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran.For this,one hundred sampling points were selected using randomly stratified methodology,and considering all geomorphic surfaces including summit,shoulder,backslope,footslope and toeslope;and soil depth was actually measured.Eleven primary and secondary topographic attributes were derived from the digital elevation model(DEM) at the study area.The result of multiple linear regression indicated that slope,wetness index,catchment area and sediment transport index,which were included in the model,could explain about 76 % of total variability in soil depth at the selected site.This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale.
基金funded by Major Projects of National Science and Technology “Large Oil and Gas Fields and CBM development”(Grant No. 2016ZX05 027)
文摘1 Introduction The Paleogene strata(with a depth of more than 2500m)in the Bohai sea is complex(Xu Changgui,2006),the reservoir buried deeply,the reservoir prediction is difficult(LAI Weicheng,XU Changgui,2012),and more
基金the Spanish Directorate General for Scientific and Technical Research(Ministerio de Economía y Competitividad)[grant number CGL2013-46387-C2-2-R]Ruben Valbuena’s work is supported by an H2020 Marie Sklodowska Curie Actions entitled‘Classification of forest structural types with LIDAR remote sensing applied to study tree size-density scaling theories’[grant number LORENZLIDAR-658180].
文摘In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more traditional LIDAR height metrics.Here the data fusion method consists of back-projecting LIDAR returns onto original MS images,avoiding co-registration errors.The prediction method is based on nonparametric imputation(the most similar neighbor).Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods.Results show improvements when using combinations of LIDAR and MS compared to using either of them alone.The MS sensor has little explanatory capacity for forest variables dependent on tree height,already well determined from LIDAR alone.However,there is potential for variables dependent on tree diameters and their density.The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity,which are best described from synergies between LIDAR heights and NDVI dispersion.Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes.Their inclusion in the predictor dataset may,however,in a few cases be detrimental to accuracy,and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.
基金supported by Hainan Province Science and Technology Special Fund,which is Research and Application of Intelligent Recommendation Technology Based on Knowledge Graph and User Portrait (No.ZDYF2020039).
文摘The user’s age and gender play a vital role within the user portrait.In view of the lack of basic attribute information,such as the age and gender of users,this paper constructs an attribute prediction method based on stacking multimodel integration.The user’s browsing and clicking history is analyzed to predict the user’s basic attributes.First,LR,RF,XGBoost,and ExtraTree were selected as the base classifiers for the first layer of the stacking framework,and the training results of the first layer were input as new training data into the second layer LightGBM for training.Experiments show that the proposed model can improve the accuracy of prediction results.
基金supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.Ltd.(No.J2023153).
文摘Graph data have extensive applications in various domains,including social networks,biological reaction networks,and molecular structures.Graph classification aims to predict the properties of entire graphs,playing a crucial role in many downstream applications.However,existing graph neural network methods require a large amount of labeled data during the training process.In real-world scenarios,the acquisition of labels is extremely costly,resulting in labeled samples typically accounting for only a small portion of all training data,which limits model performance.Current semi-supervised graph classification methods,such as those based on pseudo-labels and knowledge distillation,still face limitations in effectively utilizing unlabeled graph data and mitigating pseudo-label bias issues.To address these challenges,we propose a Semi-supervised graph Contrastive learning based on Associative Memory network and Pseudo-label Similarity(SCoAMPS).SCoAMPS integrates pseudo-labeling techniques with contrastive learning by generating contrastive views through multiple encoders,selecting positive and negative samples using pseudo-label similarity,and defining associative memory network to alleviate pseudo-label bias problems.Experimental results demonstrate that SCoAMPS achieves significant performance improvements on multiple public datasets.
