  Tailings ponds are places for storing industrial waste. Once the tailings
pond collapses, the villages nearby will be destroyed and the harmful chemicals
will cause serious environmental pollution. There is an urgent need for a
reliable forecast model, which could investigate the variation trend of
stability coefficient of tailing dam and issue early warnings. In order to fill
the gap, this work presents an hybrid network - Wavelet-based Long-Short-Term
Memory (LSTM) and Convolutional Neural Network (CNN), namely Wavelet-CNN-LSTM
netwrok for predicting the tailings pond risk. Firstly, we construct the
especial nonlinear data processing method to impute the missing value with the
numerical inversion (NI) method, which combines correlation analysis,
sensitivity analysis, and Random Forest (RF) algorithms. Secondly, a new
forecasting model was proposed to monitor the saturation line, which is the
lifeline of the tailings pond and can directly reflect the stability of the
tailings pond. After using the discrete wavelet transform (DWT) to decompose
the original saturation line data into 4-layer wavelets and de-noise the data,
the CNN was used to identify and learn the spatial structures in the time
series, followed by LSTM cells for detecting the long-short-term dependence.
Finally, different experiments were conducted to evaluate the effectiveness of
our model by comparing it with other state-of-the-art algorithms. The results
show that Wavelet-CNN-LSTM achieves the best score both in mean absolute
percentage error (MAPE), root-mean-square error (RMSE) and R 2 .
