Application of Neural Networks to Predict Daecheong Dam Water Levels

Abstract

The accurate prediction of water levels is essential for the efficient operation of hydraulic structures such as dams. However, such predictions are impossible when considering all factors affecting water level changes. Recently, with the development of artificial neural networks, there has been research on water level prediction using deep learning models. In this study, the deep learning technique of Multi-Layer Perceptron (MLP) was combined with Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) techniques, which afford advantages in time series data processing to predict the water level of Daecheong Dam. Three data preprocessing processes were applied to improve accuracy in water level predictions by taking travel time into account, adding the outflow data of the dam, and normalizing the data. Eight cases were created with the three data preprocessing processes. When normalization was performed for the data that took travel time into account and the data were then used as input data for LSTM, the best prediction result was found for the peak water level of the dam. If various other data preprocessing techniques are applied in addition to those applied in this study, RNN and LSTM will show improved prediction results.