A Comparative Study of Shallow Machine Learning Models and Deep Learning Models for Landslide Susceptibility Assessment Based on Imbalanced Data

Abstract

A landslide is a type of geological disaster that poses a threat to human lives and property. Landslide susceptibility assessment (LSA) is a crucial tool for landslide prevention. This paper’s primary objective is to compare the performances of conventional shallow machine learning methods and deep learning methods in LSA based on imbalanced data to evaluate the applicability of the two types of LSA models when class-weighted strategies are applied. In this article, logistic regression (LR), random forest (RF), deep fully connected neural network (DFCNN), and long short-term memory (LSTM) neural networks were employed for modeling in the Zigui-Badong area of the Three Gorges Reservoir area, China. Eighteen landslide influence factors were introduced to compare the performance of four models under a class balanced strategy versus a class imbalanced strategy. The Spearman rank correlation coefficient (SRCC) was applied for factor correlation analysis. The results reveal that the elevation and distance to rivers play a dominant role in LSA tasks. It was observed that DFCNN (AUC = 0.87, F1-score = 0.60) and LSTM (AUC = 0.89, F1-score = 0.61) significantly outperformed LR (AUC = 0.89, F1-score = 0.50) and RF (AUC = 0.88, F1-score = 0.50) under the class imbalanced strategy. The RF model achieved comparable outcomes (AUC = 0.90, F1-score = 0.61) to deep learning models under the class balanced strategy and ran at a faster training speed (up to 63 times faster than deep learning models). The LR model performance was inferior to that of the other three models under the balanced strategy. Meanwhile, the deep learning models and the shallow machine learning models showed significant differences in susceptibility spatial patterns. This paper’s findings will aid researchers in selecting appropriate LSA models. It is also valuable for land management policy making and disaster prevention and mitigation.