Developing a Prediction Model (Heavy Rain Damage Occurrence Probability) Based on Machine Learning

Abstract

In this study, a prediction model (heavy rain damage occurrence probability or PM-HDOP) was developed for a metropolitan area. The heavy rain damage and rainfall data were collected as dependent and independent variables, respectively. The dataset was divided into training (2005-2016) and test sections (2017). We developed the PM-HDOP using machine learning methods such as logistic regression, artificial neural network, bagging, random forest, and boosting to predict the occurrence of nonlinear natural disasters. An architectural model with the best performance was selected, and the PM-HDOP was subsequently used to predict the probability of occurrence. As a result, a boosting scheme showed the best performance in Gyeonggi-do and Seoul, and a bagging scheme showed the best performance in Incheon. If the results of this study are used to predict the occurrence of heavy rain damage, which is not currently being serviced in Korea, it is possible to effectively reduce the damages. Keywords: Machine Learning, Disaster Management, Natural Disaster, PM-HDOP