首页|A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

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The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h"golden window".However,the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge.To address this gap,this work suggests an integrated method of Crossing Graph attention network and xgBoost(CGBoost).This method contains three branches,which extract the interrelations among pixels within a slope unit,the interrelations among various slope units,and the relevance between influencing factors and landslide probability,respectively,and obtain rich and discriminative features by an adaptive fusion mechanism.Thus,the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced.As a basic module of CGBoost,the proposed Crossing graph attention network(Crossgat)could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results.Moreover,the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance,and the prediction index set is established by terrain,geology,human activity,environment,meteorology,and earthquake factors.CGBoost is applied to pre-dict landslide susceptibility in the Gorkha meizoseismal area.3.43％of coseismic landslides are randomly selected,of which 70％are used for training,and the others for testing.In the testing set.the values of Overall Accuracy,Precision,Recall,F1-score,and Kappa coefficient of CGBoost attain 0.9800,0.9577,0.9999,0.9784,and 0.9598,respectively.Validated by all the coseismic landslides,CGBoost outperforms the current major landslide susceptibility assessment methods.The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

Coseismic landslideLandslide susceptibility predictionGraph neural networkDeep learning

Qiyuan Yang、Xianmin Wang、Jing Yin、Aiheng Du、Aomei Zhang、Lizhe Wang、Haixiang Guo、Dongdong Li

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Hubei Subsurface Multi-scale Imaging Key Laboratory,School of Geophysics and Geomatics,China University of Geosciences,Wuhan 430074,China

State Key Laboratory of Biogeology and Environmental Geology,China University of Geosciences,Wuhan 430074,China

Key Laboratory of Geological and Evaluation of Ministry of Education,China University of Geosciences,Wuhan 430074,China

Laboratory of Natural Disaster Risk Prevention and Emergency Management,School of Economics and Management,China University of Geosciences,Wuh

Laboratory of Natural Disaster Risk Prevention and Emergency Management,School of Economics and Management,China University of Geosciences,Wuhan 430074,China

College of Electronic Science and Engineering,National University of Defense Technology,Changsha 410073,China

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国家自然科学基金国家自然科学基金国家自然科学基金Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of EducationOpening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of EducationState Key Laboratory of Biogeology and Environmental GeologyFundamental Research Funds for the Central Universities,China University of Geosciences,Wuhan湖南省自然科学基金

42311530065U21A201371874165GLAB2020ZR02GLAB2022ZR02GBL12107CUG26420220062021JC0009

2024

10.1016/j.gsf.2023.101770

地学前缘(英文版)
中国地质大学（北京） 北京大学

地学前缘(英文版)

CSTPCD
影响因子：0.576
ISSN：1674-9871
年,卷(期)：2024.15(2)
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