Development And Evaluation Of The Combined Machine Learning Models For The Prediction Of Dam Inflow

Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network-long short-term memory (RNN-LSTM), and convolutional neural network-LSTM (CNN-LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash-Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m(3)/s, mean absolute error (MAE) of 29.034 m(3)/s, correlation coefficient (R) of 0.924, and determination coefficient (R-2) of 0.817. However, when the amount of dam inflow is below 100 m(3)/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m(3)/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m(3)/s, MAE 18.063 m(3)/s, R 0.927, and R-2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m(3)/s, MAE 18.093 m(3)/s, R 0.912, and R-2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.