A Hybrid Multi-Criteria Decision-Making Approach for Sustainable Forest Fire Monitoring Using Unmanned Aerial Vehicles

Abstract

Unmanned aerial vehicles (UAVs) have become an effective tool for forest fire monitoring. This study evaluates UAVs for forest fire management, addressing the challenges posed by ambiguous and uncertain factors. Single-valued neutrosophic sets (SVNSs) are employed to model complex uncertainties, as they incorporate three distinct membership values: false, true, and indeterminate. The evaluation of UAVs is a multifaceted task due to the variety of factors involved. To address this complexity, multi-criteria decision-making (MCDM) methods are used. Specifically, the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are integrated with SVNS to compute UAV factor weights (via AHP) and rank the UAVs (via SVN-TOPSIS). This study examines sixteen criteria for evaluating ten UAVs to identify the optimal one for forest fire monitoring. A sensitivity analysis is conducted to assess the stability of the results, varying criteria weights and observing their impact on the rankings. The findings confirm the robustness of the proposed methodology, with stable outcomes across different scenarios. A comparative analysis with other MCDM methods (such as CODAS, VIKOR, COPRAS, MABAC, and a fuzzy framework) shows a strong correlation between the results from the proposed model and those from the alternative MCDM approaches.