Data stratification toward advanced flood waste estimation: A case study in South Korea

Abstract

Flood waste management is important for reducing the damage and secondary environmental pollution caused by delays in disaster recovery. One key issue related to flood waste management concerns estimating the precise quantity of waste to plan recovery strategies and policies. In this study, an advanced flood waste estimation technique was devised using data stratification. In total 90 flood cases in South Korea were sorted by three strata characteristics: administrative region (AR; equivalent to special city or province), urbanization rate (UR), and disaster type and coastal accessibility (DC). According to the results, such data stratification led to flood waste prediction improvement not only by the single-stage stratification but also by successive stratifications. Data stratification was effective both for identifying groups with similar contexts and for eliminating disparities in the dataset that impede accurate waste prediction. Among the stratification sequences tested, the order resulted in the most improvement in flood waste prediction was UR, AR, and DC. This stratification order yielded enhanced waste prediction in 74 cases. Since this study deals with a strategy to resolve gaps in disaster data, which is a crucial issue in many countries, it is envisaged that this strategy can be transferred to other countries.