Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis  

作　　者：A.L.Achu C.D.Aju Mariano Di Napoli Pranav Prakash Girish Gopinath E.Shaji Vinod Chandra 

机构地区：[1]Department of Climate Variability and Aquatic Ecosystems,Kerala University of Fisheries and Ocean Studies(KUFOS),Kochi,Kerala 682508,India [2]Department of Geology,University of Kerala,Thiruvananthapuram,Kerala 695581,India [3]Department of Earth,Environment and Life Sciences,University of Genoa,Corso Europa,26,Genoa 16132,Italy [4]Department of Computer Science,University of Kerala,Thiruvananthapuram,Kerala 695581,India

出　　处：《Geoscience Frontiers》2023年第6期327-340,共14页地学前缘（英文版）

基　　金：The authors would like to thank Kerala University of Fisheries and Ocean Studies(KUFOS)for providing high computing facilities(HP-Z6);granting permission to conduct the study。

摘　　要：Landslide susceptibility maps are vital tools used by decision-makers to adopt mitigation strategies for future calamities.In this context,research on landslide susceptibility modelling has become a topic of relevance and is in constant evolution.Though various machine-learning techniques(MLTs)have been identified for landslide susceptibility modelling,the uncertainty inherent in the models is rarely considered.The present study attempts to quantify the uncertainty associated with landslide prediction models by developing a new methodological framework based on the ensembles of the eight MLTs.This methodology has been tested at the highlands of the southern Western Ghats region(Kerala,India),where landslides have frequently been occurring.Fourteen landslide conditioning factors have been identified as part of this study,and their association was correlated with 671 historic landslides in the study area.The study used four ensemble models such as the mean of probabilities,the median of probabilities,the weighted mean of probabilities,and the committee average.The weighted mean of probability was proved to be the best model based on the average of 800 standalone MLTs,viz.,receiver operating characteristics,true skill statistics,and area under curve with corresponding validation scores.Thereafter,an uncertainty analysis was carried out on the coefficient of variation.A confident map was generated to represent the distinct zonation of landslide susceptibility areas with definite uncertainty scales.Nearly 74%of the past landslides fall in the higher susceptibility-low uncertainty category.It is also inferred that such micro-level zonation based on MLTs may improve the efficiency of landslide susceptibility maps and may help in accurately identifying landslide-prone areas in the future.The confident maps thus generated can be used as a ready reference to the planners for the formulation of landslide adaptation strategies at micro-scales.

关 键 词：LANDSLIDES MACHINE-LEARNING Ensemble model KERALA INDIA 

分 类 号：P642.22[天文地球—工程地质学]