Saturation Line Forecasting via a Channel and Temporal Attention-Based Network

Abstract

Tailings ponds are places for storing industrial waste. The saturation line is the crucial factor in quantifying the safety of tailings ponds. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. To mitigate this problem, in this paper, we present a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. The channel and temporal attention are utilized to capture subtle high-dimensional time-series dependence. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into the CNN-LSTM model to enhance the feature-extracting ability in the channel and temporal dimensions. Consequently, our proposed model is shown to outperform classic models on multiple real-world datasets in terms of RMSE, MAPE, R2 and MAE, respectively.

Keywords

• Deep learning
• saturation line forecasting
• LSTM network
• attention
• wavelet transform