Development of Heavy Rain Damage Prediction Function for Public Facility Using Machine Learning

2017 ◽  
Vol 17 (6) ◽  
pp. 443-450 ◽  
Author(s):  
Changhyun Choi ◽  
◽  
Kihyuck Park ◽  
Heekyung Park ◽  
Myungjin Lee ◽  
...  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Younghye Bae ◽  
...  

Prediction models of heavy rain damage using machine learning based on big data were developed for the Seoul Capital Area in the Republic of Korea. We used data on the occurrence of heavy rain damage from 1994 to 2015 as dependent variables and weather big data as explanatory variables. The model was developed by applying machine learning techniques such as decision trees, bagging, random forests, and boosting. As a result of evaluating the prediction performance of each model, the AUC value of the boosting model using meteorological data from the past 1 to 4 days was the highest at 95.87% and was selected as the final model. By using the prediction model developed in this study to predict the occurrence of heavy rain damage for each administrative region, we can greatly reduce the damage through proactive disaster management.


2018 ◽  
Vol 18 (7) ◽  
pp. 503-512 ◽  
Author(s):  
Donghyun Kim ◽  
Changhyun Choi ◽  
Jongsung Kim ◽  
Hongjun Joo ◽  
Jungwook Kim ◽  
...  

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2516 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jungwook Kim ◽  
Hung Soo Kim

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.


2017 ◽  
Vol 17 (2) ◽  
pp. 371-379 ◽  
Author(s):  
Jongsung Kim ◽  
◽  
Changhyun Choi ◽  
Jongso Lee ◽  
Hung Soo Kim ◽  
...  

2018 ◽  
Vol 18 (7) ◽  
pp. 435-447
Author(s):  
Changhyun Choi ◽  
Jongsung Kim ◽  
Donghyun Kim ◽  
Junhyeong Lee ◽  
Deokhwan Kim ◽  
...  

2017 ◽  
Vol 17 (3) ◽  
pp. 331-338 ◽  
Author(s):  
Changhyun Choi ◽  
◽  
Jongsung Kim ◽  
Jeonghwan Kim ◽  
Hanyong Kim ◽  
...  

2019 ◽  
Vol 19 (6) ◽  
pp. 115-127
Author(s):  
Jongsung Kim ◽  
Junhyeong Lee ◽  
Donghyun Kim ◽  
Changhyun Choi ◽  
Myungjin Lee ◽  
...  

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