The Systematical Solutions About Victoria’s “Fire Emergency Drones” Quantity and Location Solutions

Abstract

For more than a year, there have been severe fires in almost all Australian states, with the eastern state of Victoria in particular. This paper aims to address three specific problems in “fighting wildfires” with three mathematical models. On the basis of analyzing the data, Model 1 obtains the specific fire situation of more than 5 months at the end of 2019. Since the occurrence of fire follows Poisson distribution, we use Monte Carlo method to generate random numbers that meet such distribution for simulation. Finally, the entropy weight method + Topsis method was used to determine the optimal combination: 13 SSA UAVs and 19 repeater UAVs, and the cost was $242,000. Model 2 needs to consider how equipment increases under extreme fires. To get the worst fire scenario in 10 years, we used cellular automata to simulate the occurrence, spread and extinction of fires. For Model 3, We use the improved genetic algorithm, by means of crossover, combination mutation, position mutation and displacement mutation of genes are carried out to select a certain number of excellent individuals. The algorithm balances the performance and accuracy, and finally obtains that the layout of the UAV should be concentrated in the east side of Victoria State as far as possible. In addition, we also analyzed the robustness and sensitivity of the model, it turns out that the model is robust.