Very short-term radar rainfall prediction using deep neural network for hydropower dam operation

It is important to utilize various hydrological and weather information and accurate real-time forecasts to understand the hydrological conditions of the dam in order to make decisions of dam operation. In particular, due to rainfall concentrated in a short period of time during the flood season, it is necessary to plan the exact amount of dam discharge using real-time rainfall forecasting information. Compared to the ground rain gauge network, the radar has a high resolution of time and space, which enables the continuous expression of rainfall, which is very advantageous for very short-term prediction. Especially, In particular, the radar is capable of three-dimensional observation of the atmosphere, which has an advantage in understanding the vertical development and structure of clouds and rainfall, which can be used to observe torrential rain in the dam basin and to anticipate future rainfall intensity changes, rainfall movement and duration time. This study aims to develop a suitable radar-based very short-term rainfall prediction technique and to produce rainfall prediction information of the dam basin for stable dam operation and water disaster prevention. The radar-based rainfall prediction in this study is to be performed using a convolutional deep neural network with the 8 years weather radar data of the Korea Meteorological Administration. And, we select rainfall cases with high rainfall intensity and train the deep neural network to ensure the accuracy of flood season rainfall prediction. In addition, we intend to perform the accuracy evaluation with extrapolation-based rainfall prediction results for the dam basin.

 

This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)