Landslide susceptibility assessment based on multi GPUs: a deep learning approach

Landslide is a major natural hazard causing losses of human lives and properties. Therefore, it is significant to assess landslide susceptibility. This paper proposed an assessment model for landslide susceptibility based on deep learning to avoid landslide hazards and reduce losses. We combined the multilayer perceptron and the frequency ratio to construct a hybrid model to calculate landslide susceptibility. We used 22,877 landslide locations and an equal number of non-landslide locations obtained from high-resolution satellite images for experiments. The model’s accuracy and the AUC value outperform the non-hybrid single models by 32.88%. Furthermore, we employed multi GPUs to accelerate the training process. We utilized a node with four GPUs to distribute the model and calculate the input batch, resulting in a decent speedup.