A MACHINE LEARNING BASED SYSTEM TO CLASSIFY FOREST FIRE PREDICTION FOR FOREST SAFETY

Abstract

Forecasting forest fires requires the application of different techniques and instruments to evaluate the likelihood and potential seriousness of a fire starting in a forested region. Elements like drought, extreme temperatures, and human behaviors like bonfires, cigarettes, and pyrotechnics can all play a role in sparking forest fires. Various methods such as statistical examination, AI algorithms, and satellite pictures are employed to gather and dissect data on climate conditions, moisture levels, topography, and other factors that may heighten the chance of a forest fire. Models for predicting forest fires can furnish advanced caution systems to inform officials and locals of potential fire hazards. Predicting forest fires in the future is expected to decrease the impact of fires. This study focuses on developing a system for predicting forest fires, which calculates the likelihood of a fire starting based on various meteorological factors such as location (latitude and longitude) and temperature. The Random Forest regression algorithm was utilized to create this predictive model. Key Words: Forest Fire, Machine Learning, Temparature, Random Forest, Regression