Temporal detection of sharp landslide deformation with ensemble-based LSTM-RNNs and Hurst exponent

Abstract

The sharp slope deformation which often contains seasonal patterns is the major source of the landslide hazard with respect to the local community, which it is a serious geological environment problem. In this paper, a long short-term memory-based deep learning framework has been proposed to model the deformation behaviors especially the sharp deformation of the landslide. First, the Box–Cox transformation is applied to normalize the dataset that includes time-series deformation, precipitation, and reservoir water level. Then, an elastic net (EN)-based ensemble of long short-term memory recurrent neural networks (LSTM-RNNs) is applied to forecast landslide deformation by using month forward-chaining nested cross-validation. This method is performed on the time-series data as our training strategy. Last, the Hurst exponent is formulated to identify incoming sharp deformation. The computational results demonstrated that this approach can accurately identify future sharp deformation. The Hurst exponent illustrates the abnormal patterns in the prediction errors which indicate sharp deformation. As a result, the proposed framework would assist the on-site risk analysis and decision-making process for geological engineers to prevent the landslide hazard in the future.