Mapping landslide susceptibility and types using Random Forest

查看全文 作　　者：Khaled [1]Taalab;Tao [1]Cheng;Yang [1]Zhang

机构地区：[1]SpaceTimeLab,Department of Civil,Environmental and Geomatic Engineering,University College London,London,UK

出　　处：《Big Earth Data》索引2018年第2卷第2期,共20页

基　　金：The research has received funding from the Seventh Framework Programme,European Union Research and Development Funding Programme for research,technological development and demonstration under grant agreement No 603960-Novel Indicators for identifying critical INFRAstructure at RISK from Natural Hazards(INFRARISK www.infrarisk-fp7.eu).The third author's PhD research is jointly supported by China Scholarship Council under Grant 201603170309 and the Dean's Prize from the University College London. 摘　　要：Landslides are one of the most destructive natural hazards;they can drastically alter landscape morphology,destroy man-made struc-tures,and endanger people’s life.Landslide susceptibility maps(LSMs),which show the spatial likelihood of landslide occurrence,are crucial for environmental management,urban planning,and minimizing economic losses.To date,the majority of research into data mining LSM uses small-scale case studies focusing on a single type of landslide.This paper presents a data mining approach to producing LSM for a large,heterogeneous region that is susceptible tomultipletypesoflandslides.UsingacasestudyofPiedmont,Italy,a Random Forest algorithm is applied to produce both susceptibility maps and classification maps.These maps are combined to give a highly accurate(over 85%classification accuracy)LSM which con-tains a large amount of information and is easy to interpret.This novel method of mapping landslide susceptibility demonstrates the efficacy of Random Forest to produce highly accurate susceptibility maps for alargeheterogeneousregion withouttheneed formultiple susceptibility assessments. 关键词：Landslide susceptibility landslide type random forest

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Mapping landslide susceptibility and types using Random Forest

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