Intelligent Decision Support System for civil Protection Management in Fire Suppression Operations
Abstract
Purpose. To develop a conceptual model of an intelligent decision support system for civil protection to select response options during fire suppression by integrating risk assessment, machine learning, and multi-criteria decision-making methods.
Method. The study employed systems analysis, mathematical modeling, decision theory, risk assessment, machine learning, the Analytic Hierarchy Process (AHP), data normalization, and additive multi-criteria evaluation of alternatives.
Findings. A modular DSS architecture was developed that integrates data collection and preprocessing, hazardous-scenario prediction, risk assessment, generation of feasible alternatives, their ranking, and recommendation generation. A risk model (R = PS) was proposed. A numerical example demonstrated the feasibility of ranking response alternatives, while a ±20% sensitivity analysis did not alter the resulting ranking order.
Theoretical implications. An integrated decision-making cycle from fire development prediction to the multi-criteria selection of a response option was formalized.
Practical implications. The model can serve as a methodological basis for a specialized DSS and can be integrated with GIS, UAVs, and automated monitoring systems.
Originality / value. The study integrates machine-learning-based prediction, consequence assessment, and multi-criteria ranking of response alternatives within a unified architecture.
Limitations / future research. The model is conceptual and requires validation using real-world data, assessment of the effects of incomplete data and data drift, refinement of criterion weights, and development of a software prototype.
Paper type. Conceptual and methodological study employing mathematical modeling.
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References
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