Landslide Susceptibility Mapping Based on AI Technology
Liu Cong
Chen Yongji
Zhang Tiao
Lu Quanzhong
Abstract：Utilizing AI technology for landslide susceptibility mapping offers the advantages of efficiency and accuracy.To promote its application in landslide disaster prevention and control,in this article it introduces and summarizes the principles and characteristics of machine learning,deep learning,and ensemble learning models.Representative models such as Support Vector Machines,Deep Random Forests,and Random Forests were applied for analysis in Lueyang County,Shaanxi Province.It discusses the application and development directions of AI technology in the field of landslide susceptibility.The results indicate that ensemble learning models based on decision trees,compared to logistic regression and support vector machines,demonstrate higher efficacy with AUC values above 0.90.Under the commonly used class imbalance sampling strategy in LSM(landslide susceptibility mapping),ensemble models based on Boosting show advantages and are relatively less affected by sampling ratios.Generative Adversarial Networks can enhance the performance of deep learning models under data constraints,where in this study,the AUC value increased from 0.77 to 0.82.Combining landslide theoretical models with AI data models has great potential;leveraging AI models that fully utilize time-series data can improve model performance and help reveal the chain disaster effects and spatio-temporal evolution characteristics of landslides;conducting systematic studies of various learning models is of significant importance for the application of AI technology in landslide susceptibility mapping.
Keywords：landslide susceptibility mappingdeep learningmachine learningensemble learningengineering geology
Publication Date：2025-06-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：14( 2270-2283 )