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KMID : 1024520190280010125
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Journal of the Environmental Sciences
2019 Volume.28 No. 1 p.125 ~ p.135
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The Development of a Rainfall Correction Technique based on Machine Learning for Hydrological Applications
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Lee Young-Mi
Ko Chul-Min
Shin Seong-Cheol
Kim Byung-Sik
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Abstract
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For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.
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KEYWORD
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Heavy rainfall, Machine learning, Rainfall correction, LightGBM, XGBoost
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