Forest Fire Prediction Using Machine Learning

The world is currently grappling with the challenges of global warming, largely due to the excessive impact of rapid industrialization, population growth, and pollution, which are posing significant threats to our environment. Events such as floods, famine, cyclones, and forest fires are some of the manifestations of the detrimental effects on the environment. Therefore, there is an urgent need to take necessary steps to prevent further catastrophes. This paper focuses on predicting potential forest fires and determining their intensity. We propose an approach to reduce and prevent forest fires by identifying the factors that contribute to them and analyzing the relationships between these factors. If we can predict the potential causes at an early stage, we can take the necessary steps to mitigate the problem. We present a comprehensive method for predicting forest fires using the Random Forest Regressor (RFR), a machine-learning model. The RFR predicts the extent of forest area that could be affected by fire, in conjunction with the Fire Weather Index (FWI), providing essential information and insights. We have compared the performance of the RFR with other well-known machine learning (ML) models and have found that the RFR outperforms the others. The simulation results demonstrate the effectiveness of applying these models to the specific dataset collected from physical sensor data.