A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm

Abstract

Abstract The choice of model parameters in landslide susceptibility mapping makes a major determinant of model accuracy. The purpose of this study is to optimize the hyperparameters based on a Bayesian optimization algorithm, and to obtain a high accuracy random forest landslide susceptibility evaluation model. The research steps are detailed as follows. Firstly, taking a typical landslide prone mountainous area as an example, 16 conditioning factors, such as elevation, annual average rainfall, distance from roads, distance from buildings and so on, were preliminarily selected as the conditioning factors of landslide susceptibility. Combined with 1520 historical landslide events, a geospatial database was established with 30 m resolution. Secondly, the geospatial data sample set was constructed by random sampling according to ratio of historical landslides and non-landslides of 1:10. Based on the whole sample set, the random forest model adopted the Bayesian optimization algorithm to optimize the hyperparameters. Next, the optimal hyperparameters were selected to be trained to get the evaluation model of landslide susceptibility. In addition, they were carried out the analysis of landslide susceptibility mapping for the whole study area. After that, the recursive feature elimination method was used to screen out the dominant conditioning factors that can explain the degree of landslide susceptibility. The results indicated that the area under curve (AUC) values of receiver operating characteristic (ROC) curve in training data set, verification data set and regional simulation were 0.95, 0.87 and 0.93, respectively. 65% of the historical landslides fell between the high susceptibility and very high susceptibility regions, which made up