BN-FLEMO∆: A Bayesian Network-based Flood Loss Estimation Model for
Adaptation Planning in Ho Chi Minh City, Vietnam
Abstract
The risk of flooding is on the rise in Delta cities, such as Ho Chi Minh
City (HCMC) in Vietnam, with projections indicating further increases
due to climate change and urbanization. Flood risk analyses, for which
loss modeling is a key component, play a crucial role in decisions on
flood risk management and urban development. Probabilistic
multi-variable loss models are increasingly being used to improve loss
estimation, as they describe loss processes better and inherently
provide a quantification of uncertainties. However, such models are
often based on input variables that are determined by expert judgment.
Thus, we propose the first probabilistic multi-variable flood loss model
designed for residential buildings in delta cities such as HCMC
(BN-FLEMO∆). BN-FLEMO∆ is built upon new building-level empirical survey
data. The model is developed with an automatic machine learning-based
(ML) feature selection framework and a systematic learning process to
determine the optimal structure of the Bayesian Network. Based on a
methods comparison, we demonstrate the following key advantages of
BN-FLEMO∆: 1. enhanced, empirically-based description of flood loss
processes leading to improved accuracy in loss estimation; 2. provision
of a probability distribution of losses and inherent quantification of
modeling uncertainty; 3. network structure allows model application even
when data for one or more input variables are missing, which is
particularly valuable in data-scarce environments. We therefore expect
that BN-FLEMO∆ will significantly improve risk analyses in HCMC and
similar delta cities and support decision-makers in developing
sustainable flood risk management strategies for these dynamic
flood-prone regions.