Ecological Indicators (Sep 2024)
An integrated Bayesian networks and Geographic information system (BNs-GIS) approach for flood disaster risk assessment: A case study of Yinchuan, China
Abstract
Flooding poses severe ecological and environmental challenges, requiring comprehensive risk assessments to inform the development of sustainable management practices. This study proposes a framework that integrates Bayesian networks (BNs) and Geographic information systems (GIS) for flood disaster risk assessment, based on an evaluation indicator system consisting of three dimensions: hazard, vulnerability, and exposure. The proposed BNs-GIS model synergistically combines the probabilistic reasoning capabilities of BNs to capture interdependencies among risk factors with the spatial analysis power of GIS. The model incorporates diverse data sources, including hydrological models, remote sensing, infrastructure details, land use patterns, and ecological indicators, to generate a comprehensive risk profile. The framework was applied Yinchuan, a city in northwest China prone to flash flooding events. The model incorporated sensitivity analyses to quantify uncertainties stemming from input data and model parametrizations, to enable robust risk estimation. Our findings demonstrate the efficacy of the BNs-GIS approach in identifying areas with elevated flood risk and assessing the reliability of risk estimates. This integrated methodology contributes to ecological assessment by providing a probabilistic yet spatially-explicit evaluation of flood hazards. The study contributes to the development of indicator-based monitoring and assessment programs, supporting informed decision-making for sustainable management practices in flood-prone urban ecosystems. By providing a comprehensive understanding of flood risks, this approach can help policymakers and urban planners develop more effective and sustainable flood management strategies. While the model shows promise, limitations include its reliance on the quality and availability of input data, and the need for local expertise in model parameterization. Future work should focus on incorporating real-time data and dynamic updating capabilities to enhance the model’s applicability in rapidly changing urban environments.
