Water Quality Prediction Model Based on Temporal Attentive Bidirectional Gated Recurrent Unit Model

Hongyu Yang, Lei Guo () and Qingqing Tian
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Hongyu Yang: School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Lei Guo: School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
Qingqing Tian: School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China

Sustainability, 2025, vol. 17, issue 20, 1-28

Abstract: Water pollution has caused serious consequences for human health and aquatic systems. Therefore, analyzing and predicting water quality is of great significance for the early prevention and control of water pollution. Aiming at the shortcomings of the Gated Recurrent Unit (GRU) water quality prediction model, such as the low utilization rate of early information and poor deep feature extraction ability of the hidden state mechanism, this study combines the temporal attention (TA) mechanism with the bidirectional superimposed neural network. A time-focused bidirectional gated recurrent unit (TA-Bi-GRU) model is proposed. Taking the actual water quality data of the water source reservoir in Xiduan Village as the research object, this model was used to predict four core water quality indicators, namely pH, ammonia nitrogen (NH 3 N), total nitrogen (TN), and dissolved oxygen (DOX). Predictions are made within multiple time ranges, with prediction periods of 7 days, 10 days, 15 days, and 30 days. In the long-term prediction of the TA-Bi-GRU model, its average R 2 was 0.858 (7 days), 0.772 (10 days), 0.684 (15 days), and 0.553 (30 days), and the corresponding average MAE and MSE were both lower than those of the comparison models. The experimental results show that the TA-Bi-GRU model has higher prediction accuracy and stronger generalization ability compared with the existing GRU, bidirectional GRU (Bi-GRU), Time-focused Gated Recurrent Unit (TA-GRU), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Deep Temporal Convolutional Networks-Long Short-Term Memory (DeepTCN-LSTM) models.

Keywords: water quality prediction; water pollution prevention and control; evidence theory; time attention mechanism; multi-scale prediction; bidirectional gated cycle unit (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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