Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
1
Department of Disaster Prevention and Environmental Engineering, Kyungpook National University, 2559 Gyeongsang-daero, Sangju-si 37224, Gyeongsangbuk-do, Korea
2
Faculty of Water Resources Engineering, Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam
*
Author to whom correspondence should be addressed.
Water 2019, 11(7), 1387; https://doi.org/10.3390/w11071387
Received: 28 May 2019 / Revised: 4 July 2019 / Accepted: 5 July 2019 / Published: 5 July 2019
(This article belongs to the Special Issue Machine Learning Applied to Hydraulic and Hydrological Modelling)
Abstract
Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs. View Full-TextKeywords:
flood forecasting; Artificial Neural Network (ANN); Recurrent Neural Network (RNN); Long Short-Term Memory (LSTM); deep neural network; Da river
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MDPI and ACS Style
Le, X.-H.; Ho, H.V.; Lee, G.; Jung, S. Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water 2019, 11, 1387.
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