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

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.