Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar
Win Win Zin*1,, Akiyuki Kawasaki*2, Georg Hörmann*3, Ralph Allen Acierto*4, Zin Mar Lar Tin San*1, and Aye Myat Thu*1
*1Department of Civil Engineering, Yangon Technological University
Gyogone, Insein Road, Yangon 11011, Myanmar
*2Department of Civil Engineering, The University of Tokyo, Tokyo, Japan
*3Department of Hydrology and Water Resources Management, Kiel University, Kiel, Germany
*4Institute of Industrial Sciences, The University of Tokyo, Tokyo, Japan
Flood loss models are essential tools for assessing flood risk. Flood damage assessment provides decision makers with critical information to manage flood hazards. This paper presents a multivariable flood damage assessment based on data from residential building and content damage from the Bago flood event of July 2018. This study aims to identify the influences on building and content losses. We developed a regression-based flood loss estimation model, which incorporates factors such as water depth, flood duration, building material, building age, building condition, number of stories, and floor level. Regression approaches, such as stepwise and best subset regression, were used to create the flood damage model. The selection was based on Akaike’s information criterion (AIC). We found that water depth, flood duration, and building material were the most significant factors determining flood damage in the residential sector.
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