ZHANG M M，LI S D，WANG X，et al.Landslide susceptibility assessment Based on machine learning and encoders[J]. Journal of Henan Polytechnic University( Natural Science), doi: 10.16186/j.cnki.1673-9787.2023120035.  

Received：2023-12-12

Revised：2024-04-09

Online：2024-07-28

Landslide susceptibility assessment Based on machine learning and encoders (Online)

ZHANG Mengmeng¹，LI Shaoda¹，WANG Xiao²，LI Xinyue³，DAI Keren^1，4

（1. School of Earth and Planetary Science，Chengdu University of Technology，Chengdu 610059，Sichuan，China；2.School of Architecture and Civil Engineering，Chengdu University，Chengdu 610106，Sichuan，China；3.Mahindra United World College of India，Pune，MH 412108，India；4.State Key Laboratory of Geohazard Prevention and Geoenvironment Protection，Chengdu University of Technology，Chengdu 610059，Sichuan，China）

Abstract: Objectives To enhance the ability of machine learning models to extract data features with limited samples and improve the predictive accuracy of the models. Methods In this paper，Jiulong County，Kangding City，Luding County and Muli County，the key provincial erosion prevention areas in the middle and lower reaches of the Yalong and Dadu Rivers in Sichuan Province，were selected as the study area for landslide susceptibility evaluation，twelve influencing factors were selected to construct the landslide susceptibility evaluation index system，the coefficient of determination（CF）was used to quantify the evaluation index，and noise-reducing auto-encoders（DAEs） and convolutional auto-encoders（CAE） were added to the best-performing model by comparing the logistic regression（LR）and the support vector machine（SVM） models. Results By comparing with the CF-LR model，the CF-SVM model showed improvements of 31.9% in precision（P），1.1% in F-measure，17.1% in Kappa coefficient，8.5% in overall accuracy（OA），and 8.6% in AUC. The addition of the DAE encoder to CF-SVM increased recall（R） by 8.1%，F-measure by 5.8%，Kappa coefficient by 8.1%，and overall accuracy（OA）by 4% compared to the original CF-SVM. The addition of the CAE encoder to CF-SVM increased recall（R）by 0.4%，F-measure by 0.2%，Kappa coefficient by 0.2%，and overall accuracy（OA）by 0.1% compared to the original CF-SVM. Conclusions The CF-SVM

model has higher prediction accuracy among the selected machine learning methods. The DAE encoder added to the CF-SVM has better robustness compared to CAE encoder，thus the CF-SVM-DAE model performs the best among all models and is more suitable for the current study area.

Key words：Landslides；susceptibility modelling；LR；SVM；encoders

CLC: P642.22