Long-Term Water Temperature Forecasting in Fish Spawning Grounds Downstream of Hydropower Stations Using Machine Learning

Di Zhang, Yiming Ma, Aiping Jiang, Yufeng Ren (), Junqiang Lin (), Qidong Peng and Tiantian Jin
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Di Zhang: Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China
Yiming Ma: Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China
Aiping Jiang: China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Yufeng Ren: Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China
Junqiang Lin: China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Qidong Peng: China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Tiantian Jin: China Institute of Water Resources and Hydropower Research, Beijing 100038, China

Sustainability, 2025, vol. 17, issue 10, 1-18

Abstract: To meet the demand for water temperature prediction in the ecological operation of reservoirs, this study presents a long-term water temperature forecasting model based on machine learning algorithms using the Yidu spawning grounds, located downstream of the Three Gorges Reservoir (TGR), as a case study. The results demonstrated that the transfer learning model outperformed conventional models in terms of prediction accuracy, forecast duration, and computational efficiency. Specifically, the transfer learning model achieved an average error of 0.16–0.35 °C, outperforming the conventional model (with an error range of 0.15–0.6 °C), and exhibited superior capability in capturing complex water temperature variations. Regarding computational efficiency, the transfer learning model required significantly less training time and adapted rapidly to new data, enhancing the practical applicability of the model. During the critical ecological operation period (May–June), the transfer learning model’s average absolute error was 0.2–0.3 °C, effectively supporting optimal selection of the reservoir’s ecological operation timing. The findings provide a scientific basis for decision making in the integrated operation of reservoirs.

Keywords: water temperature; long-term forecasting; machine learning algorithms; transfer learning; Three Gorges Reservoir (TGR) (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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