Comparative Study of Artificial Neural Network and Random Forest Model for Susceptibility Assessment of Landslides Induced by Earthquake in the Western Sichuan Plateau, China

Abstract

Earthquake-induced landslides are one of the most dangerous secondary disasters in mountainous areas throughout the world. The nowcasting of coseismic landslides is crucial for planning land management, development, and urbanization in mountainous areas. Taking Wenchuan County in Western Sichuan Plateau (WPS) as the study area, a landslide inventory was built using historical records. Herein, eight causative factors were selected for a library of factors, and then a landslide susceptibility assessment (LSA) was performed based on the machine learning techniques of Random Forest (RF) and Artificial Neural Network (ANN) models, respectively. The prediction abilities of the above two LSM models were assessed using the area under curve (AUC) value of the receiver operating characteristics (ROC) curve, precision, recall ratio, accuracy, and specificity. The performances of both machine learning techniques were found to be excellent, but RF outperformed in accuracy. There were still some differences between the models’ performances shown by the results: RF (AUC = 0.966) outperformed ANN (AUC = 0.914). The RF model demonstrated a higher degree of correlation between the areas classified as very low and high susceptibility in comparison to the ANN model. The results provided a theoretical framework upon which machine learning applications could be applied (e.g., RF and ANN), a reliable and low-cost tool to assess landslide susceptibility. This comparative study will provide a useful description of earthquake-induced landslides in the study area, which can be used to anticipate the features of landslides in the future, and have played a very important role in proper anthropogenic activities, resource management, and infrastructural development of the mountainous areas.