National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

作     者：Qigen Lin Pedro Lima Stefan Steger Thomas Glade Tong Jiang Jiahui Zhang Tianxue Liu Ying Wang Qigen Lin;Pedro Lima;Stefan Steger;Thomas Glade;Tong Jiang;Jiahui Zhang;Tianxue Liu;Ying Wang

作者机构：Institute for Disaster Risk ManagementSchool of Geographical SciencesNanjing University of Information Science&TechnologyNanjing 210044China Department of Geography and Regional ResearchUniversity of ViennaUniversitätsstraße 71010 ViennaAustria Institute for Earth ObservationEurac ResearchViale Druso 139100 Bolzano-BozenItaly Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education/Academy of Disaster Reduction and Emergency ManagementFaculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China 

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：262-276页

核心收录：

学科分类：081803[工学-地质工程] 08[工学] 0818[工学-地质资源与地质工程] 

基　　金：This work was supported primarily by the National Key Research and Development Program of China(Grant Nos.2016YFA0602403,2017YFC1502505) the National Natural Science Funds(Grant No.41271544) the Startup Foundation for Introducing Talent of NUIST the Second Tibetan Plateau Scientific Expedition and Research Program(Grant Nos.2019QZKK0906,2019QZKK0606) 

主　　题：Statistical modelling Landslide susceptibility Generalized additive model Mixed-effects model China 

摘      要：China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inve