Comparative study of landslide susceptibility mapping with different recurrent neural networks

This paper aims to use recurrent neural networks (RNNs) to perform landslide susceptibility mapping in Yongxin County, China. The two main contributions of this study are summarized as follows. First, the regular RNN is compared to its three variants in the case study of landslide susceptibility mapping for the first time, including long short term memory, gated recurrent unit and simple recurrent unit. Second, a sequential data representation method is proposed to fully explore the predicting potential of RNNs. The study area consists of 364 historical landslide locations that were divided into two parts: 255 (70%) for training and 109 (30%) for validation, and 16 landslide influencing factors were considered for spatial prediction. To validate the effectiveness of these RNN-related methods, several objective measures of accuracy, recall, F-measure, Matthews correlation coefficient and the receiver operating characteristic were used for evaluation. Experimental results demonstrate that very high and high susceptible areas are concentrated in the northwest and south of Yongxin County, while landslides in the central area are less prone to occur. Based on quantitative results, all the RNN-related methods achieved area under the curve values above 0.83 and produced accurate prediction results with the optimized parameters. Therefore, the RNN framework can be used as a useful tool for the landslide susceptibility mapping task to mitigate and manage landslides.