Abstract
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 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/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 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 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.
Highlights
Global warming has led to concerns over climate change and caused the complexity of the hydrologic cycle, resulting in greater uncertainty in the management of water resources [1]
The comparison of a Gaussian linear regression model (GLM), Gaussian generalized additive models (GAMs), multivariate adaptive regression splines (MARS), artificial neural network (ANN), random forest (RF), and M5 models was analyzed to highlight the strengths and limitations of each of the models, and the results showed that GAM showed high Nash–Sutcliffe efficiency (NSE) performance, but showed a rapid increase of uncertainty with high temperatures [22]
As the performance of deep learning has recently been verified throughout data science and technology, it is believed that the deep neural network (DNN), a deep learning technique to solve the problem of numerical prediction, can contribute to improving the accuracy of the calculation of dam inflow
Summary
Global warming has led to concerns over climate change and caused the complexity of the hydrologic cycle, resulting in greater uncertainty in the management of water resources [1]. In the case of Soyang River Dam, it is difficult to perform the proper simulation; due to the watershed covering North Korean territory, the hydrological models, such as SWAT, cannot estimate dam inflow with great accuracy, due to the scarcity of the data [10] For these problems, the estimation models using simple data, such as statistical models or machine learning models, can be a solution. The artificial neural network (ANN) has been used to develop a model that predicts the flow of reservoirs up to six months in advance by learning, verifying, and evaluating the inflow datasets of Egypt’s Aswan High Dam over 130 years [14].
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