Adaptive Simulation of Separate Factors in the Alexandridis Wildfire Model

This paper provides a concise and comprehensive analysis of forest fire spread simulation. The Alexandridis model was implemented and enhanced utilizing stochastic methods in a Python environment. The impact of various factors influencing fire spread was individually studied. Results were empirically analyzed based on model-generated images, addressing the lack of real fire data. Multiple scenarios and parameter values were explored, highlighting the effectiveness of the developed method. In summary, this paper provides a concise and comprehensive analysis of forest fire spread simulation, demonstrating the efficacy of the Alexandridis model. Findings suggest the potential for developing a more effective model to combat forest fires and improve prediction and prevention methods.