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Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models
Open AccessArticle

The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

1
New Business Development Team, ECOBRAIN Co. Ltd., Jeju 63309, Korea
2
Department of Urban & Environmental Disaster Prevention Engineering, Kangwon National University, Samcheok 25913, Korea
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(1), 111; https://doi.org/10.3390/atmos11010111
Received: 9 December 2019 / Accepted: 13 January 2020 / Published: 16 January 2020
(This article belongs to the Special Issue Radar Hydrology and QPE Uncertainties)
This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts. View Full-Text
Keywords: heavy rainfall; machine learning; hydrological application; rainfall correction heavy rainfall; machine learning; hydrological application; rainfall correction
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MDPI and ACS Style

Ko, C.-M.; Jeong, Y.Y.; Lee, Y.-M.; Kim, B.-S. The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications. Atmosphere 2020, 11, 111.

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