Single-Step and Multi-Step Prediction for Dissolved Oxygen of Huangpu River Based on Feature Engineering and the BiGRU-Attention-TCN Model

Water quality prediction is an important tool for environmental protection and public health. It can prevent water pollution, maintain ecological balance, and provide crucial scientific evidence for water resource management. To address the low prediction accuracy of traditional methods, this study uses data from the water quality monitoring station on the Huangpu River in the Taihu Basin of Shanghai to predict dissolved oxygen levels. We propose a water quality prediction method based on feature engineering and the BiGRU-Attention-TCN model. The proposed method employs feature engineering to extract monitoring station status information, improving model robustness and stability. It uses Bidirectional Gated Recurrent Unit (BiGRU) to process time series data, capturing bi-directional dependencies, and applies the Attention mechanism to enhance the model's focus on key time steps and features. Additionally, Temporal Convolutional Network (TCN) is introduced to further extract long-term dependencies and global features. Our method was compared with several commonly used baseline methods, and experimental results show that the proposed method outperforms other advanced baseline methods.