Comparison of Machine Learning Algorithms for Discharge Prediction of Multipurpose Dam

Jiyeong Hong,Dongseok Yang,Kisung Kim,Gwanjae Lee,Jonggun Kim,Joo Hyun Bae,Kyoung Jae Lim,Seoro Lee

doi:10.3390/w13233369

Jiyeong Hong, Dongseok Yang + Show 6 more

Open Access

https://doi.org/10.3390/w13233369

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Abstract

For effective water management in the downstream area of a dam, it is necessary to estimate the amount of discharge from the dam to quantify the flow downstream of the dam. In this study, a machine learning model was constructed to predict the amount of discharge from Soyang River Dam using precipitation and dam inflow/discharge data from 1980 to 2020. Decision tree, multilayer perceptron, random forest, gradient boosting, RNN-LSTM, and CNN-LSTM were used as algorithms. The RNN-LSTM model achieved a Nash–Sutcliffe efficiency (NSE) of 0.796, root-mean-squared error (RMSE) of 48.996 m3/s, mean absolute error (MAE) of 10.024 m3/s, R of 0.898, and R2 of 0.807, showing the best results in dam discharge prediction. The prediction of dam discharge using machine learning algorithms showed that it is possible to predict the amount of discharge, addressing limitations of physical models, such as the difficulty in applying human activity schedules and the need for various input data.

Highlights

IntroductionThere will be greater variability of floods and droughts, occurring more frequently and longer; water resource management will become more important [1]
The results of the heatmap analysis indicated that the discharge and the inflow of the previous days had an impact on the discharge of the multipurpose dam
The results of this study showed that the discharge amount of the Soyang River Dam was effectively simulated using machine learning algorithms

Summary

Introduction

There will be greater variability of floods and droughts, occurring more frequently and longer; water resource management will become more important [1]. In Korea, where rainfall is concentrated in the summer, a water resource and ecosystem management plan in the downstream water system should be systematically established in advance by predicting the amount of discharge from the upstream multipurpose dam. For this purpose, there is a need for a method that can efficiently predict the amount of discharge from multipurpose dams mediated by artificial effects

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