Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest

Abstract

This study aims to develop two optimized models of landslide susceptibility mapping (LSM), i.e., logical regression (LR) and random forest (RF) models, premised on hyperparameter optimization using the Bayesian algorithm, and compare their applicability in a typical landslide-prone area (Fengjie County, China). First, data for 1520 historical landslides occurring was collected from field investigations and literature reviews, to construct a spatial database of 16 conditioning factors. Subsequently, the Bayesian algorithm was adopted to optimize the hyperparameters of the LR and RF models, premised on the dataset of all cells (including landslides and non-landslides). Finally, the two optimized models were estimated and compared with the area under curve (AUC) and confusion matrix. Based on the Bayesian algorithm, the AUC value of the test dataset in LR model is improved by 4%, while the AUC value of the test dataset in RF model is improved by 10%, indicating that both models' hyperparameter optimization premised on the Bayesian algorithm have delivered considerable impact on the accuracy of the models; so hyperparameter optimization is very important for models of LSM. Although both models exhibit reasonable performances, the optimized RF model premised on hyperparameter optimization has a better stability and predictive capability in case area. These findings make up for the crucial step in LSM (hyperparameter optimization) through the Bayesian algorithm, and provide a comparison case between LR and RF models after comprehensive consideration of hyperparameter optimization, so as to increase the convincing power of the comparison of these models and provide a knowledge base for model comparison: comparison premised on hyperparameter optimization.