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 database.The effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine model.The receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant models.For the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,respectively.In 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,respectively.The 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.
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].
