Development of Heavy Rain Damage Prediction Model Using Machine Learning Based on Big Data

• Published in: Advances in meteorology Vol. 2018; no. 2018; pp. 1 - 11
• Main Authors: Bae, Younghye, Kim, Donghyun, Kim, Jongsung, Kim, Jeonghwan, Choi, Changhyun, Kim, Hung Soo
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Analysis; Atmospheric precipitations; Big data; Climate change; Data management; Decision trees; Dependent variables; Disaster management; Emergency preparedness; Floods; Heavy rainfall; Machine learning; Mathematical models; Meteorological data; Prediction models; Rain; Rainfall; Regression analysis; Socioeconomic factors; Storm damage; Weather forecasting; United States > US
• ISSN: 1687-9309; 1687-9317
• DOI: 10.1155/2018/5024930

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.