Forest Fire Prediction Using Machine Learning Techniques

Abstract

Forest Fire Prediction is a key component of forest fire control. This is a major environmental problem that creates ecological destruction in the form of a threatened landscape of natural resources that disrupts the stability of the ecosystem, increases the risk for other natural hazards, and decreases resources such as water that causes global warming and water pollution. Fire Detection is a key element for controlling such incidents. Prediction of forest fire id expected to reduce the impact of forest fire in the future. Many fire detection algorithms are available with different approach towards the detection of fire. In the existing work processes the fire affected region is predicted based on the satellite images. To predict the occurrences of a forest fire the proposed system processes using the meteorological parameters such as temperature, rain, wind and humidity were used. Random forest regression and Hyperparameter tuning using RandomizedSearchCV algorithm we used a various sub-samples of dataset on which it fits several decision trees and uses averaging to improve the predictive accuracy and control over-fitting. Based on the analysis of the models with all the selected meteorological parameters can represent the forest fire events. This paper discusses about a comparative study of different models for predicting forest fire such as Decision Tree, Random Forest, Support Vector Machine, Artificial Neural networks (ANN) algorithms. The study of calculation of RandomizedSearchCV coefficient using Hyperparameter tuning gives best results of Mean absolute error(MAE) 0.03, Mean squared error(MSE) 0.004, Root mean squared error(RMSR) 0.07