End-to-end time-dependent probabilistic assessment of landslide hazards using hybrid deep learning simulator

Early warning detection of landslide hazards often requires real-time or near real-time predictions, which can be challenging due to the presence of multiple geo-uncertainties and time-variant external environmental loadings. The propagation of these uncertainties at the system level for understanding the spatiotemporal behavior of slopes often requires time-consuming numerical calculations, significantly hindering the establishment of an early warning system. This paper presents a hybrid deep learning simulator, which fuses p arallel c onvolutional neural networks (CNNs) and l ong short-term memory (LSTM) networks through a ttention mechanisms, termed PCLA-Net, to facilitate time-dependent probabilistic assessment of landslide hazards. PCLA-Net features two novelties. First, it is capable of simultaneously handling both temporal and spatial information. CNNs specialize in interpreting spatial data, while LSTM excels in handling time-variant data. Coupled with two attention mechanisms, the two modules are combined to probabilistically predict the spatiotemporal behavior of slopes. Second, PCLA-Net realizes end-to-end predictions. In this paper, the Liangshuijing landslide in the Three Gorges Reservoir area of China is used to illustrate PCLA-Net. It is first validated followed by a comparison with existing techniques to demonstrate its improved predictive capabilities. The proposed PCLA-Net simulator can achieve the same level of accuracy with at least 50% reduction in computation resources.