Open AccessForest Wildfire Monitoring and Communication UAV System Based on Particle Swarm Optimization
Author(s) -
Jerry Zhijian Yang,
Juncheng Qian,
Hong-Wei Gao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1982/1/012068
Subject(s) - drone , terrain , computer science , particle swarm optimization , repeater (horology) , firefighting , key (lock) , environmental science , real time computing , environmental resource management , computer security , geography , artificial intelligence , encoding (memory) , machine learning , genetics , cartography , biology
Forest fire is one of the major disasters faced by the society at present. During the drought every year, devastating wildfires often occur all over the world, causing serious economic losses and life injuries. The effective layout of unmanned aerial vehicles in the monitoring system is the key to effectively prevent forest wildfires. Wildfires occur suddenly and rapidly, and most of them occur in dense forest areas which are difficult for firefighters to reach. Considering the factors of economy, security and capability, an efficient and accurate integrated optimization layout model of UAV is proposed. The UAV monitoring system based on this model can make the emergency operation centre best guide the active crew through two-way radio communication, so as to achieve the best effect and maximum safety. We optimized the number and combination of drones by using optimization algorithm, and got the highest scoring drone procurement plan in different types of areas. For example, when meeting low terrain without obstacles, the optimal number of drones for SSA is 20 and for Radio Repeater is 1 in high risk. Finally, we analysed the influence of global climate change, fire scale, UAV density and terrain on the optimal location of UAV.