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Article

Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins

1
Department of Civil & Environmental Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul 143-747, Korea
2
Faculty of Water Resources Engineering, Thuyloi University, 175 Tay Son, Dong Da, Ha Noi 116705, Vietnam
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Astray
Water 2021, 13(6), 776; https://doi.org/10.3390/w13060776
Received: 12 February 2021 / Revised: 4 March 2021 / Accepted: 9 March 2021 / Published: 12 March 2021
(This article belongs to the Special Issue The Application of Artificial Intelligence in Hydrology)
Radar-based rainfall forecasts are widely used extrapolation algorithms that are popular in systems of precipitation for predicting up to six hours in lead time. Nevertheless, the reliability of rainfall forecasts gradually declines for heavy rain events with lead time due to the lack of predictability. Recently, data-driven approaches were commonly implemented in hydrological problems. In this research, the data-driven models were developed based on the data obtained from a radar forecasting system named McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE) and ground rain gauges. The data included thirteen urban stations in the five metropolitan cities located in South Korea. The twenty-five data points of MAPLE surrounding each rain station were utilized as the model input, and the observed rainfall at the corresponding gauges were used as the model output. The results showed superior capabilities of long short-term memory (LSTM) network in improving 180-min rainfall forecasts at the stations based on a comparison of five different data-driven models, including multiple linear regression (MLR), multivariate adaptive regression splines (MARS), multi-layer perceptron (MLP), basic recurrent neural network (RNN), and LSTM. Although the model still produced an underestimation of extreme rainfall values at some examined stations, this study proved that the LSTM could provide reliable performance. This model can be an optional method for improving rainfall forecasts at the stations for urban basins. View Full-Text
Keywords: post-processed radar forecasts; quantitative precipitation forecast; long short-term memory; urban basins post-processed radar forecasts; quantitative precipitation forecast; long short-term memory; urban basins
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MDPI and ACS Style

Nguyen, D.H.; Kim, J.-B.; Bae, D.-H. Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins. Water 2021, 13, 776. https://doi.org/10.3390/w13060776

AMA Style

Nguyen DH, Kim J-B, Bae D-H. Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins. Water. 2021; 13(6):776. https://doi.org/10.3390/w13060776

Chicago/Turabian Style

Nguyen, Duc H.; Kim, Jeong-Bae; Bae, Deg-Hyo. 2021. "Improving Radar-Based Rainfall Forecasts by Long Short-Term Memory Network in Urban Basins" Water 13, no. 6: 776. https://doi.org/10.3390/w13060776

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