Deep Learning for Water Quality Prediction—A Case Study of the Huangyang Reservoir

• Published in: Applied sciences 2024-10, Vol.14 (19), p.8755
• Main Authors: Chen, Jixuan, Wei, Xiaojuan, Liu, Yinxiao, Zhao, Chunxia, Liu, Zhenan, Bao, Zhikang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Aquatic resources; Case studies; Chaos theory; China; Computational linguistics; Deep learning; Forecasts and trends; Industrial development; Language processing; Linear model; Machine learning; Management; Natural language interfaces; Neural networks; Pollution control; Rivers; Statistical methods; Surface water; System theory; Trends; Water; Water pollution; Water quality; water quality data; water quality prediction; Water resources
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14198755

Water quality prediction is a fundamental prerequisite for effective water resource management and pollution prevention. Accurate predictions of water quality information can provide essential technical support and strategic planning for the protection of water resources. This study aims to enhance the accuracy of water quality prediction, considering the temporal characteristics, variability, and complex nature of water quality data. We utilized the LTSF-Linear model to predict water quality at the Huangyang Reservoir. Comparative analysis with three other models (ARIMA, LSTM, and Informer) revealed that the Linear model outperforms them, achieving reductions of 8.55% and 10.51% in mean square error (MSE) and mean absolute error (MAE), respectively. This research introduces a novel method and framework for predicting hydrological parameters relevant to water quality in the Huangyang Reservoir. These findings offer a valuable new approach and reference for enhancing the intelligent and sustainable management of the reservoir.