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A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

Geoscience Frontiers 2024年第2期15卷 349-365页

作者： Qiyuan Yang Xianmin Wang Jing Yin Aiheng Du Aomei Zhang Lizhe Wang Haixiang Guo Dongdong Li Hubei Subsurface Multi-scale Imaging Key Laboratory School of Geophysics and GeomaticsChina University of GeosciencesWuhan 430074China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhan 430074China Key Laboratory of Geological and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Laboratory of Natural Disaster Risk Prevention and Emergency Management School of Economics and ManagementChina University of GeosciencesWuhan 430074China College of Electronic Science and Engineering National University of Defense TechnologyChangsha 410073China

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 *** 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 ***,the difficulty of susceptibility modeling under a small number of coseismic landslides can be *** 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 ***,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 *** is applied to predict 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 *** 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,*** by all the coseismic landslides,CGBoost outperforms the current major landslide susceptibility assessment *** suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

关键词： coseismic landslide landslide susceptibility prediction Graph neural network Deep learning

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Application of logistic regression model for hazard assessment of landslides caused by the 2012 Yiliang Ms 5.7 earthquake in Yunnan Province,China

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Journal of Mountain Science 2023年第3期20卷 657-669页

作者： JIN Jia-le CUI Yu-long XU Chong ZHENG Jun MIAO Hai-bo School of Civil Engineering and Architecture Anhui University of Science and TechnologyHuainan 232001China National Institute of Natural Hazards Ministry of Emergency Management of ChinaBeijing 100085China Key Laboratory of Compound and Chained Natural Hazards Dynamics Ministry of Emergency Management of ChinaBeijing 100085China Department of Civil Engineering Zhejiang UniversityHangzhou 310058China

Accurate assessment of seismic landslides hazard is a prerequisite and foundation for postdisaster relief of *** Ms 5.7 earthquake occurring on September 7,2012,in Yiliang County,Yunnan Province,China,triggered hundreds of *** explore the characteristics of coseismic landslides caused by this moderate-strong earthquake and their significance in predicting seismic landslides regionally,this study uses an artificial visual interpretation method based on a planet image with 5-m resolution to obtain the information of the coseismic landslides and establishes a coseismic landslide database containing data on 232 *** influencing factors of landslides were selected for this study:elevation,relative elevation,slope angle,aspect,slope position,distance to river system,distance to faults,strata,and peak ground *** real probability of coseismic landslide occurrence is calculated by combining the Bayesian probability and logistic regression *** on the coseismic landslides,the probabilities of landslide occurrence under different peak ground acceleration are predicted using a logistic regression ***,the model established in this paper is used to calculate the landslide probability of the Ludian Ms 6.5 earthquake that occurred in August 2014,78.9 km away from the macro-epicenter of the Yiliang *** probability is verified by the real coseismic landslides of this earthquake,which confirms the reliability of the method presented in this *** study proves that the model established according to the seismic landslides triggered by one earthquake has a good effect on the seismic landslides hazard assessment of similar magnitude,and can provide a reference for seismic landslides prediction of moderate-strong earthquakes in this region.

关键词： Yiliang earthquake coseismic landslide Logisticregression model Bayesian probability Hazard assessment

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Near real-time spatial prediction of earthquake-induced landslides:A novel interpretable self-supervised learning method

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International Journal of Digital Earth 2023年第1期16卷 1885-1906页

作者： Xuewen Wang Xianmin Wang Xinlong Zhang Lizhe Wang Haixiang Guo Dongdong Li Hubei Subsurface Multi-scale Imaging Key Laboratory School of Geophysics and GeomaticsChina University of GeosciencesWuhanPeople’s Republic of China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhanPeople’s Republic of China Key Laboratory of Geological and Evaluation of Ministry of Education China University of GeosciencesWuhanPeople’s Republic of China Laboratory of Natural Disaster Risk Prevension and Emergency Management School of Economics and ManagementChina University of GeosciencesWuhanPeople’s Republic of China College of Electronic Science and Engineering National University of Defense TechnologyChangshaPeople’s Republic of China

Near real-time spatial prediction of earthquake-induced landslides(EQILs)can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake;thus,EQIL prediction is very crucial to the 72-hour‘golden window’for *** work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau,a famous seismically-active zone,and proposes a novel interpretable self-supervised learning(ISeL)method for the near real-time spatial prediction of *** new method innovatively introduces swap noise at the unsupervised mechanism,which can improve the generalization performance and transferability of the model,and can effectively reduce false alarm and improve accuracy through *** interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial *** results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning ***,according to the interpretable module in the ISeL method,the critical controlling and triggering factors are *** ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability.

关键词： coseismic landslide near real-time interpretable artificial intelligence self-supervised learning spatial distribution prediction

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Preliminary identification of earthquake triggered multi-hazard and risk in Pleret Sub-District(Yogyakarta,Indonesia)

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Geo-Spatial Information Science 2021年第2期24卷 256-278,I0006页

作者： Aditya Saputra Christopher Gomez Ioannis Delikostidis Peyman Zawar-Reza Danang Sri Hadmoko Junun Sartohadi Faculty of Geography Universitas Muhammadiyah SurakartaCentral JavaIndonesia Graduate School of Maritime Sciences Laboratory of Volcanic Risks at Sea Kobe UniversityKobeJapan Geography Department School of ScienceUniversity of CanterburyChristchurchNew Zealand Geography Faculty Universitas Gadjah MadaYogyakartaIndonesia Department of Soil Science Faculty of AgricultureUniversitas Gadjah MadaYogyakartaIndonesia

Yogyakarta is one of the large cities in Central Java,located on Java Island,*** city,and the Pleret sub-district,where the study has taken place,is prone to earthquake hazards,because it is close to several seismically active zones,such as the Sunda Megathrust and the active fault known as the Opak *** a devastating earthquake of 2006,the population of the Pleret sub-district has increased ***,the housing demand has increased,and so is the pace of low-cost housing that does not meet earthquake-safety requirements,and furthermore are often located on unstable *** local alluvial material covering a jigsaw of unstable blocks and complex slope is conditions that can amplify the negative impacts of *** this context,this study is aiming to assess the multi-hazards and risks of earthquakes and related secondary hazards such as ground liquefaction,and coseismic *** achieve this,we used geographic information systems and remote sensing methods supplemented with outcrop study and existing seismic data to derive shear-strain *** results have revealed the presence of numerous uncharted active faults with movements visible from imagery and *** that the middle part of the study area has a complex geological structure,indicated by many unchartered faults in the *** this newly mapped blocks combined with shear strain data,we reassessed the collapse probability of buildings that reach level>0.75 near the Opak River,in central Pleret *** the buildings and from population distribution,we could determine that the highest risk was during nighttime as the buildings susceptible to fall are predominantly housing *** secondary hazards follow a slightly different distribution with a concentration of risks in the West.

关键词： Earthquake multi-hazard and risk coseismic landslide outcrop study liquefaction

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Resonance effect existence or not for landslides triggered by 2008 Wenchuan earthquake:A reply to the comment by *** Chong and Xu Xiwei

Resonance effect existence or not for landslides triggered b...

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中国科学院地质与地球物理研究所2012年度（第12届）...

作者： Shengwen Qi Qiang Xu Hengxing Lan Bing Zhang Jianyou Liu Key Lab.of Engineering Geomechanics Institute of Geology and GeophysicsChinese Academy of Sciences State Key Lab.of Geo-hard Prevention and Geo-environment Protection Chengdu University of Technology State Key Laboratory of Resources and Environmental System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences Centre for Earth Observation and Digital Earth Chinese Academy of Sciences

We are very grateful to receive these remarks about our *** our paper,we found that more landslides occurred in the block located between F2 and F3 than that in the hanging wall of the seismogenic fault of F2(see Figure 6 in our paper) based on a database of 13,085 landslides in a distributed area of 418.85 km(the smallest landslide in this inventory covers an area of 306.5 m),and we thought that this is maybe the result of resonance of the block between F2 and F3 under the combination movement of the seismogenic faults F2 and F3 and then the motion there is *** their discussion,Xu and Xu (2012)point out that there is no resonance effect of the block between F2 and F3 combination movement,or the resonance effect is rather

关键词： coseismic landslide Wenchuan earthquake coseismic fault surface rupture

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