Multi-hazard susceptibility prediction is an important component of disasters risk management plan.An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions.However,with the rapid development of artificial intelligence technology,multi-hazard sus-ceptibility prediction techniques based on machine learning has encountered a huge bottleneck.In order to effectively solve this problem,this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks(CNN).First,we use histor-ical flash flood,debris flow and landslide locations based on Google Earth images,extensive field surveys,topography,hydrology,and environmental data sets to train and validate the proposed CNN method.Next,the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria,i.e.,coefficient of determination,overall accuracy,mean absolute error and the root mean square error.Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods,debris flows and landslides.Finally,the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map.It can be observed from the map that 62.43％of the study area are prone to hazards,while 37.57％of the study area are harmless.In hazard-prone areas,16.14％,4.94％and 30.66％of the study area are susceptible to flash floods,debris flows and landslides,respectively.In terms of concurrent hazards,0.28％,7.11％and 3.13％of the study area are susceptible to the joint occurrence of flash floods and debris flow,debris flow and landslides,and flash floods and landslides,respectively,whereas,0.18％of the study area is subject to all the three hazards.The results of this study can benefit engineers,disaster managers and local government officials involved in sustain-able land management and disaster risk mitigation.

Kashif Ullah;Yi Wang;Zhice Fang;Lizhe Wang;Mahfuzur Rahman

Institute 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;School of Computer Science,China University of Geosciences,Wuhan 430074,China;Hubei Key Laboratory of Intelligent Geo-Information Processing,China University of Geosciences,Wuhan 430074,China;Department of Civil Engineering,International University of Business Agriculture and Technology(IUBAT),Dhaka 1230,Bangladesh

地学前缘（英文版）

[1]Kashif Ullah;Yi Wang;Zhice Fang;Lizhe Wang;Mahfuzur Rahman-.Multi-hazard susceptibility mapping based on Convolutional Neural Networks)[J].地学前缘（英文版）,2022(05):59-74

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