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Abstract

Micro-businesses are important sources of livelihood for low- and middle-income households. In Ho Chi Minh City (HCMC), Vietnam, many micro-businesses are set up on the ground floor of residential houses susceptible to urban floods. Increasing flood risk in HCMC threatens the financial resources of micro-businesses by damaging business contents and causing business interruptions. Since flood loss estimations are rarely conducted at an object-level resolution and are often focused on households or large companies, the commercial losses suffered by micro-businesses are often overlooked. This study aims to derive the drivers of flood losses [%] for micro-businesses by applying a conditional random forest to survey data (relative business content losses: n=317; relative business interruption losses: n=361) collected from micro-businesses in HCMC. The variability in the losses of business contents and losses due to business interruption were adequately explained by the revenue of the businesses from monthly sales, the age of the building where the business is established, and the hydrological characteristics of the flood. Based on the identified drivers, probabilistic loss models (nonparametric Bayesian networks) were developed using a combination of data-driven and expert-based model formulation. The models estimated the flood losses for HCMC's micro-businesses with a mean absolute error of 3.8 % for content losses (observed mean: 4.7 %, Q50: 0.0) and 18.7 % for business interruption losses (observed mean: 18.2 %, Q50: 10). The Bayesian network model for business interruption had similar predictive performance when it was regionally transferred and applied to comparable survey data from another Vietnamese city, Can Tho. The flood loss models introduced in this study make it possible to derive flood risk metrics specific to micro-businesses to support adaptation decision-making and risk transfer mechanisms.