Simulation of long-term rainfall runoff using a Long Short-Term Memory (LSTM) networks: Case of Osipcheon watershed in Korea

Water resource management requires long-term historical discharge data, and physical hydrology models were widely used. Recently, in the field of water resources, various studies using artificial neural networks have been conducted. In this paper, long-term discharge was estimated using meteorological data and LSTM (Long Short-Term Memory). Study area is selected as Osipcheon watershed in Korea. Observed meteorological data and discharge data were collected for 10 years to training period (2011-2018) and testing period (2019–2020). The potential evaporation data was calculated by Hargreaves formula equation. And NSE (Nash– Sutcliffe Efficiency), RMSE (Root Mean Square Error), and MSE (Mean Square Error) were used to compare LSTM results and observed discharge during the training, test and total period. As a result, NSE, RMSE, and MSE were satisfactory during the total period which showed a high possibility of using the LSTM deep learning technique in the water resource area.

Acknowledgment: This research was supported by a grant(2022-MOIS61-001) of Development Risk Prediction Technology of Storm and Flood For Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).