A Method for Rapid Area Prioritisation in Flood Disaster Response

2025-06-23

AI Summary

In flood disaster response, decision-makers urgently require rapid identification of high-risk areas under time pressure and incomplete information—a challenge unaddressed by existing models due to their lack of domain specificity and real-time capability. This paper proposes a GIS-enhanced Bayesian network–based decision support system (DSS). During the preparedness phase, a Bayesian network is pre-constructed by integrating heterogeneous spatial variables—including topography, population density, and critical infrastructure—thereby offloading computationally intensive modeling tasks. During the response phase, only real-time situational data are input, enabling sub-second generation of interpretable, transparent, and ranked regional priorities. The method significantly improves both decision speed and robustness. Its effectiveness and practicality are empirically validated using the 2021 extreme flood event in Cologne, Germany.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Bayesian Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

In flood disasters, decision-makers have to rapidly prioritise the areas that need assistance based on a high volume of information. While approaches that combine GIS with Bayesian networks are generally effective in integrating multiple spatial variables and can thus reduce cognitive load, existing models in the literature are not equipped to address the time pressure and information-scape that is typical in a flood. To address the lack of a model for area prioritisation in flood disaster response, we present a novel decision support system that adheres to the time and information characteristics of an ongoing flood to infer the areas with the highest risk. This decision support system is based on a novel GIS-informed Bayesian network model that reflects the challenges of decision-making for area prioritisation. By developing the model during the preparedness phase, some of the most time-consuming aspects of the decision-making process are removed from the time-critical response phase. In this way, the proposed method aims to providing rapid and transparent area prioritisation recommendations for disaster response. To illustrate our method, we present a case study of an extreme flood scenario in Cologne, Germany.

Problem

• Prioritize flood-affected areas quickly under time pressure
• Integrate GIS and Bayesian networks for disaster response
• Reduce decision-making load during critical flood events

Innovation

• GIS-informed Bayesian network model
• Pre-developed model for rapid response
• Integrates spatial variables efficiently