Tailings Pond Risk Prediction Using Long Short-Term Memory Networks

Abstract

Tailings ponds are a major hazard, and are ranked 18th in the risk assessment of world accident hazards. The saturation line height is one of the most important factors that affects the safety of tailings ponds. Due to the extremely complicated seepage boundary conditions of tailings ponds, a precise calculation method is urgently needed for predicting the saturation lines. Therefore, the dynamic model should be investigated to evaluate the potential for dam breakage. In this paper, based on an analysis of tailings ponds in various regions, we use the long short-term memory (LSTM) algorithm to predict the time-series variation of the saturation line height. To evaluate and validate our model, we compare with traditional models. The results demonstrate that the deep learning method significantly outperforms the traditional methods, provides a new strategy and has significant potential for tailings ponds safety prediction.