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

Changhyun Choi,Jeonghwan Kim,Donghyun Kim,Younghye Bae,Jongsung Kim,Hung Soo Kim

doi:10.1155/2018/5024930

Abstract

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.

Highlights

Previous studies that were used in predicting and preparing for natural disaster damage in advance mostly performed linear regression analysis using weather factors such as precipitation, rainfall intensity, maximum wind speed, and hurricane central pressure that cause natural disasters and damage through floods, rainstorms, and hurricanes [2–11]
The present study relies on the meteorological big data provided by the Korea Meteorological Administration to arrive at a list of various explanatory variables that account for the occurrence of heavy rain damage and uses machine learning—known to have higher prediction performance than regression models—to develop functions that can predict heavy rain damage in advance
In order to develop our prediction model for heavy rain damage using machine learning based on big data, we selected the Seoul Capital Area as the study area and constructed response and explanatory variables from the data on heavy rain damage amounts given in the Annual Natural Disaster Report and the meteorological big data collected from the Korea Meteorological Administration’s Open Weather Data Portal

Summary

Introduction

Previous studies that were used in predicting and preparing for natural disaster damage in advance mostly performed linear regression analysis using weather factors such as precipitation, rainfall intensity, maximum wind speed, and hurricane central pressure that cause natural disasters and damage through floods, rainstorms, and hurricanes [2–11]. The present study relies on the meteorological big data provided by the Korea Meteorological Administration to arrive at a list of various explanatory variables that account for the occurrence of heavy rain damage and uses machine learning—known to have higher prediction performance than regression models—to develop functions that can predict heavy rain damage in advance. For this purpose, we constructed a response variable and explanatory variables for the study area of our study and used various machine learning models such as decision trees, bagging, random forests, and boosting to develop prediction models for heavy rain damage based on big data. All the methods used here are supervised learning techniques, which use their own algorithms to generate rules that best explain the response variables

Methods

Results

Conclusion