Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques

Sangwon Lee,Jaekwang Kim

doi:10.3390/w13172447

Abstract

The Soyang Dam, the largest multipurpose dam in Korea, faces water resource management challenges due to global warming. Global warming increases the duration and frequency of days with high temperatures and extreme precipitation events. Therefore, it is crucial to accurately predict the inflow rate for water resource management because it helps plan for flood, drought, and power generation in the Seoul metropolitan area. However, the lack of hydrological data for the Soyang River Dam causes a physical-based model to predict the inflow rate inaccurately. This study uses nearly 15 years of meteorological, dam, and weather warning data to overcome the lack of hydrological data and predict the inflow rate over two days. In addition, a sequence-to-sequence (Seq2Seq) mechanism combined with a bidirectional long short-term memory (LSTM) is developed to predict the inflow rate. The proposed model exhibits state-of-the-art prediction accuracy with root mean square error (RMSE) of 44.17 m3/s and 58.59 m3/s, mean absolute error (MAE) of 14.94 m3/s and 17.11 m3/s, and Nash–Sutcliffe efficiency (NSE) of 0.96 and 0.94, for forecasting first and second day, respectively.

Highlights

Water 2021, 13, 2447. https://Due to its high population density, South Korea has only one-sixth of the world’s average water available per capita and suffers from deterioration of water resource quality, floods, and droughts due to significant variance in yearly regional and seasonal precipitation [1]
We propose an end-to-end model that consists of a Seq2Seq algorithm incorporated with bi-directional long short-term memory (LSTM) and a scaled exponential linear unit (SELU) activation function to predict the inflow rate over a period of two days
We introduce the following two primary components that form the foundation of our model: a bidirectional LSTM and a sequence-to-sequence model

Summary

Introduction

Water 2021, 13, 2447. https://Due to its high population density, South Korea has only one-sixth of the world’s average water available per capita and suffers from deterioration of water resource quality, floods, and droughts due to significant variance in yearly regional and seasonal precipitation [1]. Islands and mountainous areas suffer from annual water shortages that require the use of emergency water supplies with restrictions on water usage These shortages are due to low water inflow, on tributary streams with delayed investment in infrastructure, causing an increase in damage of water-related natural disasters [1,2]. To overcome these issues, Korea has constructed multipurpose dams to manage water resources. RNNs suffer from a vanishing gradient problem as the length of the sequence increases To overcome this problem, LSTM uses gated functions to accept long sequences and decide which part of the input data to remember [34]. Some researchers have used the LSTM to predict the inflow rate [15,16]

Methods

Results

Conclusion