Single-Valued Neutrosophic MCDM Approaches Integrated with MEREC and RAM for the Selection of UAVs in Forest Fire Detection and Management

Abstract

In recent times, the world has experienced a rise in the frequency of forest fires. These fires cause severe economic damage and pose a significant threat to human lives. Therefore, it is essential to search for solutions that can help combat fires and detect them early. Once a fire reaches a certain level, it becomes challenging to control it. Various systems have been proposed to collect data and detect forest fires, such as satellites and other traditional methods. However, these solutions have been ineffective in terms of cost, coverage of large areas, accuracy, and the safety of human lives. To address these limitations, Unmanned Aerial Vehicles (UAVs) or drones have been used for detecting, combatting, and early warning of forest fires. UAVs are one of the modern technologies that have achieved great progress in monitoring natural disasters and have been widely used in monitoring, detecting, and predicting fires. They can fly without a human pilot on board, which makes them ideal for preserving human life. In addition, they are equipped with firefighting tools and various tools for remote sensing. This is to take high-quality photos or videos of the area to be detected. Different types of UAVs are used to fight fires, and here decision-makers face a problem in choosing between these types. Therefore, this research proposes a new MCDM model integrated with neutrosophic sets for selecting the optimal UAV to combat forest fires; therefore it helps in effectively detecting and fighting the fire. The proposed model integrates a Method based on Removal Effects of Criteria (MEREC) and Root Assessment Method (RAM) with the context of neutrosophic sets that effectively deal with ambiguity for selecting the optimal UAV which use in the detection and combat forest fires.