Evaluation of Landslide Susceptibility Based on Multi-objective Optimization Method

Abstract

[Objective] In the landslide susceptibility assessment, the selection and optimization of the landslide prediction model are very important to the efficiency of the calculation process and the accuracy of the prediction results. Aiming at the problems that the existing single-objective genetic optimization algorithm (Genetic Algorithm, GA) is prone to premature maturity, poor local search ability, and slow global optimization speed, this paper develops a new optimization algorithm framework, which integrates the classic algorithm in the multi-objective genetic algorithm-Non-dominated sorting method with elite selection strategy (NSGA-II) combined with common machine learning algorithms, random forest (RF), and support vector machine (SVM) to predict landslide susceptibility. Different from single objective optimization, NSGA-II algorithm can perform feature selection and hyper-parameter optimization simultaneously, and make the prediction model achieve optimal accuracy, recall, precision and AUC (area under curve, AUC) at the same time. [Methods] Taking the Chongqing section of the Three Gorges reservoir area as the study area, the four optimized models (RF-GA, SVM-GA, RF-NSGA-II. and SVM-NSGA-II) were compared and analyzed in three aspects: model accuracy evaluation, landslide hazard susceptibility zoning map, and zoning statistics. [Results] NSGA-II was more effective than GA optimization, and in terms of model evaluation and landslide susceptibility zoning, the RF-NSGA-II model had higher predictive performance, with four evaluation values of 80.91%, 81.89%, 80.07% and 88.60% respectively, proving the effectiveness of the NSGA-II optimization algorithm; the area share of very low to very high hazard zones were in the order of 23.06%, 22.46%, 22.96%, 19.99%, and 11.53%, which verified the reliability of the RF-NSGA-II model. The susceptibility map predicted by the RF-NSGA-II model showed that the high and extremely high susceptibility areas were concentrated in the north and distributed in bands from east to west. [Conclusion] RF-NSGA-II algorithm based on multi-objective selection provides a new idea for the optimization of machine learning model for landslide risk assessment.

Keywords

• landslide susceptibility
• multi-objective optimization
• random forest
• support vector machine