首页|Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models
Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models
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Landslide susceptibility assessment is crucial in predicting landslide occurrence and potential risks.However,traditional methods usually emphasize on larger regions of landsliding and rely on relatively static environmental conditions,which exposes the hysteresis of landslide susceptibility assessment in refined-scale and temporal dynamic changes.This study presents an improved landslide susceptibility assessment approach by integrating machine learning models based on random forest(RF),logical regression(LR),and gradient boosting decision tree(GBDT)with interferometric synthetic aperture radar(InSAR)technology and comparing them to their respective original models.The results demonstrated that the combined approach improves prediction accuracy and reduces the false negative and false pos-itive errors.The LR-InSAR model showed the best performance in dynamic landslide susceptibility assess-ment at both regional and smaller scale,particularly when identifying areas of high and very high susceptibility.Modeling results were verified using data from field investigations including unmanned aerial vehicle(UAV)flights.This study is of great significance to accurately assess dynamic landslide sus-ceptibility and to help reduce and prevent landslide risk.
Yingdong Wei、Haijun Qiu、Zijing Liu、Wenchao Huangfu、Yaru Zhu、Ya Liu、Dongdong Yang、Ulrich Kamp
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Shaanxi Key Laboratory of Earth Surface and Environmental Carrying Capacity,College of Urban and Environmental Sciences,Northwest University,Xi'an 710127,China
Insitute of Earth Surface System and Hazards,College of Urban and Environmental Sciences,Northwest University,Xi'an 710127,China
Earth and Environmental Sciences Discipline,Department of Natural Sciences,University of Michigan-Dearborn,Dearborn,MI 48104,USA
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