Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

作     者：Alireza Arabameri Fatemeh Rezaie Subodh Chandra Pal Artemi Cerda Asish Saha Rabin Chakrabortty Saro Lee Alireza Arabameri;Fatemeh Rezaie;Subodh Chandra Pal;Artemi Cerda;Asish Saha;Rabin Chakrabortty;Saro Lee

作者机构：Department of GeomorphologyTarbiat Modares UniversityTehran 14117-13116Iran Geoscience Platform Research DivisionKorea Institute of Geoscience and Mineral Resources(KIGAM)124 Gwahak-ro Yuseong-guDaejeon 34132South Korea Department of Geophysical ExplorationKorea University of Science and Technology217 Gajeong-ro Yuseong-guDaejeon 34113South Korea Department of GeographyThe University of BurdwanBardhamanWest Bengal 713104India Soil Erosion and Degradation Research GroupDepartament de GeografiaUniversitat de ValènciaBlasco Ibàñez2846010-ValenciaSpain 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2021年第12卷第6期

页      面：129-146页

核心收录：

学科分类：083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 081802[工学-地球探测与信息技术] 08[工学] 0708[理学-地球物理学] 0818[工学-地质资源与地质工程] 09[农学] 0903[农学-农业资源与环境] 081602[工学-摄影测量与遥感] 0816[工学-测绘科学与技术] 0704[理学-天文学] 

主　　题：Digital elevation model(DEM) Gully erosion susceptibility(GES) Advanced land observation satellite(ALOS) Cforest Cubist Elastic net 

摘      要：The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)*** unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral *** the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully ***,topography is highly correlated with other geo-environmental factors ***,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully ***,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)*** this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,*** research focuses on the comparison of predicted GES result by using three types of DEM *** Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different *** further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity *** methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES ***’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and *** of the model’s result was performed through area under curve(AUC)and other statistical *** valid