DRONE SELECTION FOR FOREST SURVEILLANCE AND FIRE DETECTION USING INTERVAL VALUED NEUTROSOPHIC EDAS METHOD

Forest fires are one of the major causes for deforestation resulting in significant economic and environmental losses. The application of drones has been extended to various areas including disaster management. Since drones offer numerous advantages like real-time surveillance, task planning capabilities and autonomy, they are utilized in early detection systems for forest fires. The selection of a drone type for this purpose involves a complex system of multiple factors and conflicting information, for which the use of multi-criteria decision-making (MCDM) methods have been found to be yielding effective results. The aim of this study is to present a decision framework for drone selection problem in the context of forest fire surveillance and detection. This study contributes by (i) pointing out to the gap that the drone selection problem for forest surveillance and fire detection has been sparsely addressed, (ii) presenting an extensive literature review, (iii) extracting the relevant criteria through a literature review and interviews with the experts in field, (iv) assessing the alternatives by the proposed framework based on interval valued neutrosophic evaluation based on distance from average solution (IVN EDAS) method. The proposed framework is demonstrated by a case study consisting of four drone alternatives and 14 criteria. In accordance with the extant literature, the criteria related to the visual capabilities and diagnosis are evaluated as the most crucial features. A sensitivity analysis is carried out to check for the robustness by varying the criteria weights and a comparative analysis is conducted with interval valued neutrosophic technique for preference by similarity to the ideal solution (IVN TOPSIS) and interval valued neutrosophic combinative distance-based assessment (IVN CODAS) methods to validate the veracity of the method.