Review and clustering of scientific publications on fire and rescue units response research

Purpose. This paper reviews methods and approaches used for modeling operational response processes of fire and rescue units to fires and emergencies, based on the analysis and clustering of scientific publications on this topic. The main objective is to identify the most relevant and effective modeling and analysis methods that can enhance the quality and timeliness of decision-making during fire suppression and emergency response. The tasks involved ranking publications by key scientific metrics and performing cluster analysis to identify research groups with the highest significance and practical application. The results obtained can be used to define tasks for further improvement of decision support systems in fire and rescue units response operations. Methods. The study employed methods of statistical analysis, ranking, and cluster analysis of publications based on citation metrics, Hirsch index, impact factor, Science Index, view counts, download counts, and page number. Findings. Distinct groups of authors and publications were identified based on their level of scientific activity and significance. The most relevant research directions were determined: mathematical and simulation modeling, machine learning, data mining, and stochastic modeling. Research application field. The results can be used to define research objectives in the field of enhancing decision support systems for fire and rescue unit response operations. Conclusions. Cluster analysis enabled the structuring of publications and the identification of key studies with high scientific impact, as well as the most relevant methods, models, and information technologies, thereby facilitating the selection of effective solutions in the field of operational response for fire and rescue units.