Stimulation of Forest Fire Using AIML Techniques

The increasing frequency and intensity of forest fires have created an urgent need for advanced systems capable of predicting, modelling, and mitigating wildfire risks. This study explores the simulation of forest fires using Artificial Intelligence and Machine Learning (AI/ML) techniques, integrating environmental variables such as temperature, humidity, wind speed, vegetation density, and historical fire patterns. Machine learning models-including random forests, gradient boosting, and deep neural networks-are employed to identify key fire-triggering factors and forecast fire spread dynamics. Additionally, spatial data from remote sensing and geographic information systems (GIS) are incorporated to generate realistic fire-behaviour simulations. The proposed AI/ML-driven framework enhances the accuracy and speed of fire prediction compared to traditional mathematical or physics-based models, enabling real-time scenario generation and early-warning insights. Results demonstrate the potential of AI/ML techniques to support decision-making in wildfire management, optimize resource allocation, and reduce environmental and socio-economic impacts.