Emergency Response Inference Mapping (ERIMap): A Bayesian network-based method for dynamic observation processing

TL;DR

ERIMap is a Bayesian network-based approach designed to rapidly process uncertain and conflicting information during emergencies, providing decision-makers with a dynamic, spatially-resolved map of key variables to support timely responses.

Contribution

This paper introduces ERIMap, a novel Bayesian network method tailored for emergency situations to systematically process heterogeneous data and reduce decision-making complexity.

Findings

Successfully demonstrated in a chemical plant gas leak case study

Enables real-time updating of emergency situation maps

Reduces cognitive load for emergency responders

Abstract

In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in decisions. Currently, there is a lack of systematic approaches for information processing and situation assessment which meet the particular demands of emergency situations. To address this gap, we present a Bayesian network-based method called ERIMap that is tailored to the complex information-scape during emergencies. The method enables the systematic and rapid processing of heterogeneous and potentially uncertain observations and draws inferences about key variables of an emergency. It thereby reduces complexity and cognitive load for decision makers. The output of the ERIMap method is a dynamically evolving and spatially resolved map of beliefs about key variables of an emergency that is updated each time a new observation becomes available. The method is illustrated in a case study in which an emergency response is triggered by an accident causing a gas leakage on a chemical plant site.