Spatiotemporal modelling of rainfall-induced landslides using machine learning
Citation
Ng, C.W.W., Yang, B., Liu, Z.Q., Kwan, J.S.H., Chen, L. (2021). Spatiotemporal modelling of rainfall-induced landslides using machine learning. Landslides, 18(7): 2499-2514. Link to paper
Abstract
Natural terrain landslides are mainly triggered by rainstorms in Hong Kong, which pose great threats to life and property. To mitigate landslide risk, building a prediction model which could provide information on both spatial and temporal probabilities of landslide occurrence is essential but challenging. In this study, a spatiotemporal probability model is developed with consideration of both the spatial susceptibility and the temporal activity of landslides under rainstorms. Five machine learning methods, including logistic regression, random forest, adaboost tree, support vector machine, and multilayer perceptron, are utilized and compared. Validated against historical rainstorms, the machine learning powered landslide prediction model could reasonably forecast the occurrence of landslides in a spatiotemporal context.