Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data
Joint Authors
Choi, Changhyun
Kim, Jeonghwan
Kim, Jongsung
Kim, Donghyun
Bae, Younghye
Kim, Hung Soo
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Choi, Changhyun& Kim, Jeonghwan& Kim, Jongsung& Kim, Donghyun& Bae, Younghye& Kim, Hung Soo. 2018. Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data. Advances in Meteorology،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1118772
Modern Language Association (MLA)
Choi, Changhyun…[et al.]. Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data. Advances in Meteorology No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1118772
American Medical Association (AMA)
Choi, Changhyun& Kim, Jeonghwan& Kim, Jongsung& Kim, Donghyun& Bae, Younghye& Kim, Hung Soo. Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data. Advances in Meteorology. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1118772
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118772