TY - JOUR
T1 - Emergency Response Inference Mapping (ERIMap)
T2 - A Bayesian network-based method for dynamic observation processing
AU - Schneider, Moritz
AU - Halekotte, Lukas
AU - Comes, Tina
AU - Lichte, Daniel
AU - Fiedrich, Frank
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Bayesian network
KW - Decision support system
KW - Emergency response
KW - GIS
KW - Situation awareness
UR - http://www.scopus.com/inward/record.url?scp=85209947377&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2024.110640
DO - 10.1016/j.ress.2024.110640
M3 - Article
AN - SCOPUS:85209947377
SN - 0951-8320
VL - 255
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 110640
ER -