Model-Based Decision Support System for Improving Emergency Response

Abstract

A Mobile-Based Ontology Integrated Decision Support System was built in an effort to improve the transparency in disaster situations. Using hypothetical chemical hazard scenarios, the decision-making accuracy and time taken by incident commanders using the Incident Command System was compared to incident commanders without the use of the system. The existing design of incident command systems limits the ability to manage workload and results in a limited span of knowledge. Sixteen firefighters were tested using the mobile-based ontology integrated decision support system. The results discussed in this study indicate that the information presented by the decision support system significantly improved commander’s decision-making. Potential applications of this research include Strengthening disaster risk reduction and management capacity through improved preparedness; Advancing regional and national initiatives for disaster response and risk reduction; and providing the foundation for future policies and procedures that can be easily implemented within the ICS framework.

Disclosure statement

No potential competing interest was reported by the authors.

Data availability statement

Raw data were generated at Wright State University. Derived data supporting the findings of this study are available from the corresponding author MN on request.

Additional information

Funding

This work was supported by the NSF CPS EAGER: Intelligent Agent Incident Command System Augmentation under Grant 1528550.

Notes on contributors

Meenakshi Nagarajan

Meenakshi Nagarajan was a graduate research assistant in the Interactions Design and Modeling Lab. Her research work focuses on developing simulation models to improve human performance. Her email address is [email protected]

Subhashini Ganapathy

Subhashini Ganapathy is an associate professor and chair in the Department of Biomedical, Industrial and Human Factors Engineering at Wright State University. Her research work spans core areas of mobile computing, cognitive modeling, decision-making, user-experience, and human factors engineering. Her email address is [email protected]

Michelle Cheatham

Michelle Cheatham earned a PhD in Computer Science and Engineering from Wright State University in 2014. She is currently an Associate Professor in the Computer Science and Engineering Department at WSU. Her research interests are in knowledge representation and reasoning, NLP, and voice-assisted applications. Her email address is [email protected]