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http://dx.doi.org/10.9723/jksiis.2022.27.6.077

A Study on the Real-Time Risk Analysis of Heavy-Snow according to the Characteristics of Traffic and Area  

KwangRim, Ha ((주)씨에스리 AI엔지니어링사업부)
YongCheol, Jung ((주)씨에스리 데이터엔지니어링팀)
JinYoung, Yoo ((주)씨에스리 데이터엔지니어링팀)
JunHee, Lee ((주)씨에스리 데이터엔지니어링팀)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.27, no.6, 2022 , pp. 77-93 More about this Journal
Abstract
In this study, we present an algorithm that analyzes the risk by reflecting regional characteristics for factors affected by direct and indirect damage from heavy-snow. Factors affected by heavy-snow damage by 29 regions are selected as influencing variables, and the concept of sensitivity is derived through the relationship with the amount of damage. A snow damage risk prediction model was developed using a machine learning (XGBoost) algorithm by setting weather conditions (snow cover, humidity, temperature) and sensitivity as independent variables, and setting the risk derived according to changes in the independent variables as dependent variables.
Keywords
Hazard; Heavy-snow Sensitivity; Local Sensitivity; Traffic Sensitivity;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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