Abstract
The paper presents a generalized net (GN) model of the process of terrain observation with the help of unmanned aerial vehicles (UAVs) for the prevention and rapid detection of wildfires. Using a GN, the process of monitoring a zone (through a UAV, which is further called a reconnaissance drone) and the localization of forest fires is described. For a more indepth study of the terrain, the reconnaissance drone needs to coordinate with a second UAV, called a specialized drone, so that video and sensory information is provided to the supervising fire command operational center. The proposed GN model was developed to assist in the decision-making process related to the coordination of the operation of both UAVs under dynamically changing terrain circumstances, such as those related to preventing or quickly containing wildfires. It describes the stages (transitions), logical determinants (transition predicate matrices), and directions of information flow (token characteristics) within the process of localization of fires using the pair of reconnaissance and specialized drones.
Highlights
With the recent occurrence of forest fires at unprecedented locations, such as behind the Arctic Circle, and unusual periods of the year, with threatening frequency and severity [1], it is critically important to find the exact localization of the origin, the velocity of fire spread, and to perform risk analysis for the environment, people, settlements, and critical infrastructure, in order to ensure that timely and accurate information is provided for deciding on how to best extinguish the fire and suppress its spread
unmanned aerial vehicles (UAVs) capabilities to acquire visual sensory information from a given fire zone depend on the parameters of the chosen flight platform, the available data transfer and control communications, and on information-acquisition tools, such as surveillance cameras and used sensors [32,33]
To model the process of using UAVs for the early detection of forest fires, this paper proposes the use of a generalized net
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The technology and availability of the most common, namely, so-called drones, are quickly developing, and it is estimated that they will number 1.5 million by 2023, according to research by Business Insider Intelligence [14] Their use for various purposes is growing. The facility of obtaining GN models demonstrates the flexibility and efficiency of generalized nets as a modelling tool in different fields, e.g., artificial intelligence (neural networks, expert systems, decision making, pattern recognition, etc.), biotechnology, medicine, optimization, and intercriterion analysis [18,29]. We employed GN capabilities for description and modelling in order to investigate how to make better use of existing technological advancements. This is the first generalized net model devoted to the topic considered here. The model allows for following in real time how the process unfolds, and for further complexification
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