Prediction model of the outflow temperature from stratified reservoir regulated by stratified water intake facility based on machine learning algorithm

Abstract

Temperature rhythm changes in outflows after reservoir construction cause thermal pollution in downstream rivers, which is unfavorable to the ecological health of downstream rivers. Stratified water intake facilities can effectively mitigate the impact of thermal pollution. However, there is a lack of scientific guidance to ensure that stratified water intake facilities are optimized and meet downstream water temperature requirements; therefore, an efficient and accurate method of predicting outflow temperatures is urgently needed. Based on the influence mechanism of the outflow temperature and the maximal information coefficient, a new machine learning model for predicting the outflow temperature of thermally stratified reservoirs is constructed. The vertical water temperature in front of the dam, outflow quantity, stoplog gate height and submergence depth are used as inputs. Based on prototype observation data, the prediction performance of support vector regression (SVR), K-nearest neighbors (KNN) and the multilayer perceptron neural network (MLPNN) methods is compared. The results show that the three machine learning models can predict the outflow temperature very well. Among them, the SVR model using the radial basis function (RBF) as the kernel function displays the best performance; its mean absolute error for the test set is 0.112 °C, the root mean square error is 0.143 °C, and the Nash-Sutcliffe efficiency coefficient is 0.989. A test of RBF-SVR verifies that it can effectively identify the rules and relationships between the input and output in small-sample training cases and is suitable for solving the nonlinear problem of predicting reservoir outflow temperatures. In addition, RBF-SVR display universal application value. It can not only provide a 1–10 day early warning regarding outflow temperatures but also achieve a good modeling effect for Wudongde Reservoir, which is outside the study area. Overall, the outflow temperatures of thermally stratified reservoirs are efficiently and accurately predicted, and the proposed method provides an effective reference and scientific guidance for adaptive reservoir management.