A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction

TL;DR

This paper introduces a hybrid Wavelet-CNN-LSTM model for predicting tailings pond risks, combining advanced data processing and deep learning techniques to improve early warning accuracy and environmental safety.

Contribution

The work presents a novel hybrid neural network model with specialized data imputation and wavelet denoising for tailings pond risk prediction, filling a gap in early warning systems.

Findings

Wavelet-CNN-LSTM outperforms other algorithms in MAPE, RMSE, and R2.

The model effectively captures long-term dependencies in tailings pond data.

The proposed method enhances early warning accuracy for tailings pond stability.

Abstract

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 .