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Open AccessArticle

Development of Combined Heavy Rain Damage Prediction Models with Machine Learning

1
Institute of Water Resources System, Inha University, Michuhol-Gu, Incheon 22212, Korea
2
Department of Statistics, Ewha Womans University, Seodaemun-gu, Seoul 03760, Korea
3
Department of Civil Engineering, Inha University, Michuhol-Gu, Incheon 22212, Korea
*
Author to whom correspondence should be addressed.
Water 2019, 11(12), 2516; https://doi.org/10.3390/w11122516
Received: 27 October 2019 / Revised: 22 November 2019 / Accepted: 25 November 2019 / Published: 28 November 2019
Adequate forecasting and preparation for heavy rain can minimize life and property damage. Some studies have been conducted on the heavy rain damage prediction model (HDPM), however, most of their models are limited to the linear regression model that simply explains the linear relation between rainfall data and damage. This study develops the combined heavy rain damage prediction model (CHDPM) where the residual prediction model (RPM) is added to the HDPM. The predictive performance of the CHDPM is analyzed to be 4%–14% higher than that of HDPM. Through this, we confirmed that the predictive performance of the model is improved by combining the RPM of the machine learning models to complement the linearity of the HDPM. The results of this study can be used as basic data beneficial for natural disaster management.
Keywords: disaster management; heavy rain damage; machine learning; natural disaster; prediction model; residual prediction model disaster management; heavy rain damage; machine learning; natural disaster; prediction model; residual prediction model
MDPI and ACS Style

Choi, C.; Kim, J.; Kim, J.; Kim, H.S. Development of Combined Heavy Rain Damage Prediction Models with Machine Learning. Water 2019, 11, 2516.

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