A Method for Rapid Area Prioritisation in Flood Disaster Response

TL;DR

This paper introduces a rapid decision support system combining GIS and Bayesian networks to prioritize flood-affected areas efficiently during emergencies, demonstrated through a Cologne case study.

Contribution

It presents a novel GIS-informed Bayesian network model designed specifically for time-critical flood disaster response prioritization.

Findings

Effective rapid prioritization of flood areas demonstrated in case study

Model reduces decision-making time during flood response

Provides transparent and data-driven area rankings

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.