Optimization of the resources management in fighting wildfires

Area of Science:

• Environmental Science
• Computer Science
• Operations Research

Background:

• Wildfires cause significant economic, social, and environmental damage, particularly in Mediterranean climates.
• High intensity and frequency of wildfires necessitate advanced fire spread and management models.
• Developing real-time fire-extinguishing models is challenging due to the chaotic nature of environmental systems.

Purpose of the Study:

• To propose a method for optimizing wildfire fighting resource allocation to minimize losses.
• To develop a system that adapts to the dynamic and chaotic behavior of wildfires.
• To provide a decision-making tool for effective wildfire management.

Main Methods:

• Utilized discrete simulation algorithms and Bayesian optimization for discrete and continuous problems (simulated annealing, Bayesian global optimization).
• Applied fast calculus algorithms for rapid optimization outcomes, ensuring model predictions align with real-time fire behavior, resource status, and meteorological data.
• Incorporated adaptive algorithms to account for wildfire's chaotic nature, enabling system updates with real-time data for new optimal solutions.

Main Results:

• The proposed method demonstrated effectiveness in optimizing resource allocation for wildfire suppression.
• Fast calculus and adaptive algorithms facilitated timely and accurate predictions, aligning with actual fire dynamics.
• The system proved to be a valuable tool for decision-making in wildfire management.

Conclusions:

• The developed optimization method effectively minimizes losses associated with wildfires.
• The adaptive and fast-calculating algorithms enhance the system's ability to manage chaotic wildfire behavior.
• The tool is beneficial for supporting strategic decisions in wildfire fighting operations, as shown by its application in Madrid.