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Landslide susceptibility measures the probability of landslides occurring under certain geo-environmental conditions and is essential in landslide hazard assessment. Landslide susceptibility mapping (LSM) using data-driven methods applies statistical models and geospatial data to show the relative propensity of slope failure in a given area. However, due to the rarity of multi-temporal landslide inventory, conventional data-driven LSMs are primarily generated by spatial causative factors, while the temporal factors remain limited. In this study, a spatiotemporal LSM is carried out using machine learning (ML) techniques to assess rainfall-induced landslide susceptibility. To achieve this, two landslide inventories are collected for southwestern Pennsylvania: a spatial inventory and a multi-temporal inventory, with 4543 and 223 historical landslide samples, respectively. The spatial inventory lacks the information to describe landslide temporal distribution; there are insufficient samples in the temporal inventory to represent landslide spatial distribution. A novel paradigm of data augmentation through non-landslide sampling based on domain knowledge is applied to leverage both spatial and temporal information for ML modeling. The results show that the spatiotemporal ML model using the proposed data augmentation predicts well rainfall-induced landslides in space and time across the study area, with a value of 0.86 of the area under the receiver operating characteristic curve (AUC), which makes it an effective tool in rainfall-induced landslide hazard mitigation and forecasting.
Xiong et al. (Mon,) studied this question.