Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong—An Exploratory Study

Abstract

Practical landslide predictions are instrumental to effective landslide risk management. Recently, the use of machine learning (ML) has become a promising alternative means for landslide predictions. This paper discusses the recent progress of a pilot study of ML-powered rainfall-based natural terrain landslide susceptibility analysis in Hong Kong. This study is different to other similar studies in that: (1) data sampling commonly used to deal with an imbalanced dataset is not adopted, and (2) the incorporation of domain knowledge on landslide characteristics for the development of physically meaningful ML models. The results are found to be promising, with the achieved ROC AUC up to 91.5% based on the testing data. The resolution of the susceptibility map is enhanced by approximately three orders of magnitude further than the introduction of additional features critically selected with feature engineering and based on domain knowledge and past experiences.