Bayesian approach in estimating flood waste generation: A case study in South Korea

Abstract

Accurate estimations of flood waste generation are a crucial issue in disaster waste management. Multilinear regression of related parameters has been recognized as a promising technique for flood waste estimation. There are two types of flood waste estimation methods: pre-event predictions using factors related to regional properties and rainfall hazards, and post-event predictions using damage variables due to floods, such as the number of damaged buildings. Previous attempts to establish these models used deterministic approaches; however, probabilistic methods have never been applied. Considering the large degrees of uncertainty in waste generation from floods, a probabilistic approach can provide a more accurate model compared to models developed by the conventional deterministic approach. This study applied Bayesian inference to develop a flood waste regression model in South Korea. The aims of the study are as follows: (1) to analyze the characteristics of coefficients estimated by the Bayesian approach; (2) evaluate the performance of the prediction model by Bayesian inference; and (3) assess the effectiveness of Bayesian updating in a flood waste estimation. According to the results, the coefficients obtained via Bayesian inference showed a more significant p-value compared to those developed through the deterministic approach. Bayesian inference with a null prior distribution was effective in error reduction, specifically for post-event prediction. Bayesian updating did not effectively increase the accuracy of the model, while iterative updating required a complex calculation process. These results reveal the potential of the Bayesian approach in flood waste estimations, which can be transferred to other countries.