Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms

作     者：Zhuo Chen Danqing Song 

作者机构：College of Civil EngineeringSichuan Agricultural UniversityDujiangyanPeople’s Republic of China Sichuan Higher Education Engineering Research Center for Disaster Prevention and Mitigation of Village ConstructionSichuan Agricultural UniversityDujiangyanPeople’s Republic of China School of Civil Engineering and TransportationSouth China University of TechnologyGuangzhouPeople’s Republic of China State Key Laboratory of Subtropical Building ScienceSouth China University of TechnologyGuangzhouPeople’s Republic of China 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2023年第16卷第1期

页      面：3384-3416页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by China Postdoctoral Science Foundation:[grant number 2020M680583] National Natural Science Foundation of China[grant number 52208359] National Natural Science Foundation of China:[grant number 52109125] National Postdoctoral Program for Innovative Talents:[grant number BX20200191] 

主　　题：Landslide susceptibility convolutional neural network beluga whale optimization coati optimization algorithm hybrid models Sichuan Province 

摘      要：Landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study s main,purposes are to explore the potential applications of convolutional neural networks(CNN)hybrid ensemble metaheuristic optimization algorithms,namely beluga whale optimization(BWO)and coati optimization algorithm(COA),for landslide susceptibility mapping in Sichuan Province(China).For this aim,fourteen landslide conditioning factors were compiled in a spatial *** effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine *** receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant *** the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,*** terms of the validation dataset,the CNN-COA model exhibited a higher AUC value of 0.919,while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805,*** results indicate that the CNN-COA model,followed by the CNN-BWO model,and the CNN model,offers the best overall performance for landslide susceptibility analysis.