Spatiotemporal modelling of rainfall-induced landslides using machine learning

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 paper, real-time rainfall conditions are incorporated into landslide prediction through a unique rainstorm-based database of reported landslides. Other landslide controlling factors related to topography, geology, and land cover are also considered. 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. Moreover, the effects of different rainstorm characteristics in terms of distinct rainfall spatial distribution and intensity on landslide susceptibility could also be captured by this model. For the landslide controlling factors investigated, rolling rainfall factors are proven to play a more important role than antecedent rainfall factors for landslide prediction. Among the five machine learning methods, the random forest model yields the most promising results in terms of all performance indicators (i.e., classification accuracy, recall, precision, area under curve, and overall accuracy).

Automated summary

This study incorporates real-time rainfall conditions into landslide prediction models, considering factors such as terrain, geology, and land cover. Rolling rainfall factors were found to be more important for landslide prediction than antecedent rainfall factors. Among the five machine learning methods used, the random forest model showed the most promising results in terms of various performance indicators.