Application of Machine Learning in Predicting the Water Temperature Released from Reservoirs

The release of low-temperature water from a reservoir can have negative impacts on downstream fish spawning and crop growth in irrigation areas. Therefore, predicting the discharged water temperature accurately and swiftly is crucial. This study focused on the Pubugou Hydropower Station, a major project situated on the Dadu River in the upper reaches of the Yangtze River, and evaluated the impacts of meteorological factors and reservoir operational parameters on the released water temperature using Spearman correlation coefficients (R). To predict the discharged water temperature of Pubugou Reservoir, five models were optimized by genetic algorithms including random forests, support vector regression, convolutional neural network, long short-term memory network, and the lightweight gradient boosting machine respectively. The results showed that: (1)The dew point temperature exhibited the highest correlation with discharged water temperature (R = 0.89), However, the correlation coefficient between wind speed, cloud cover, solar radiation, dam front water level, and discharge water temperature was not found to be 0.4. (2) All the five models optimized by genetic algorithms performed well on the training set, especially the random forest model (R² = 0.997). The worst performing model is the long short-term memory network model (R² = 0.985). (3) In the prediction of discharge water temperature, all models have good fitting effects, with r² greater than 0.93, average absolute error not greater than 0.662 ℃, and mean square error not greater than 0.852 ℃. Random forest models and lightweight gradient boosting machine models have shown good performance on the most of sample data, with a small residual range, while support vector regression models and convolutional neural network models have smaller maximum residuals. This research indicated that machine learning methods can effectively predict water temperature released from reservoirs, providing more reliable decision support for formulating relevant measures to alleviate the impact of reservoir discharge water temperature.