Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping

Abstract

Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides and has been under investigation for years. Although new machine learning algorithms have achieved excellent performance in terms of prediction accuracy, a sufficient quantity of training samples is essential. In contrast, it is hard to obtain enough landslide samples in most the areas, especially for the county-level area. The present study aims to explore an optimization model in conjunction with conventional unsupervised and supervised learning methods, which performs well with respect to prediction accuracy and comprehensibility. Logistic regression (LR), fuzzy c-means clustering (FCM) and factor analysis (FA) were combined to establish four models: LR model, FCM coupled with LR model, FA coupled with LR model, and FCM, FA coupled with LR model and applied in a specific area. Firstly, an inventory with 114 landslides and 10 conditioning factors was prepared for modeling. Subsequently, four models were applied to LSP. Finally, the performance was evaluated and compared by k-fold cross-validation based on statistical measures. The results showed that the coupled model by FCM, FA and LR achieved the greatest performance among these models with the AUC (Area under the curve) value of 0.827, accuracy of 85.25%, sensitivity of 74.96% and specificity of 86.21%. While the LR model performed the worst with an AUC value of 0.736, accuracy of 77%, sensitivity of 62.52% and specificity of 72.55%. It was concluded that both the dimension reduction and sample size should be considered in modeling, and the performance can be enhanced by combining complementary methods. The combination of models should be more flexible and purposeful. This work provides reference for related research and better guidance to engineering activities, decision-making by local administrations and land use planning.

Keywords

• complementary model
• unsupervised and supervised learning
• landslide susceptibility prediction
• GIS