Machine learning based on landslide susceptibility assessment with Bayesian optimized the hyperparameters

Abstract

In machine learning-based landslide susceptibility assessment, there are some differences in the evaluation results obtained by using different hyperparameters. This paper aims to use the Bayesian algorithm to optimize the hyperparameters of four common machine learning models (logistic regression, support vector machine, artificial neural network and random forest) and to explore the optimization effect of this algorithm. Taking the landslide susceptibility assessment of four counties (Anhua, Xinhua, Taojiang, and Taoyuan Counties) in central Hunan as an example, the feasibility and applicability of the algorithm are illustrated. Based on the landslide inventory, 1 017 landslide points in the study area were determined, and 15 landslide influencing factors were selected to construct the training set and test set. The Bayesian optimization algorithm is used to optimize the main hyperparameters of the four machine learning models, and four optimal models are established according to the optimized hyperparameters. The AUC value and other indicators are used to compare the predictive ability of different models. The results show that ① the prediction performance of the hyperparameters optimized models is better than that of the unoptimized models. ② Among the four optimization models, the coupling model of the random forest and Bayesian optimization algorithm has the best prediction performance