Forest Fire Detection using Machine Learning

Fire accidents have become a very major issue for the mankind and also for the wildlife. It has also been a reason for raising pollution level and global warming too. One of the major disasters affecting the environment is the forest fire which spreads out in wider area and causes lethal damage. It not only affects the flora and fauna but also affects the atmosphere by increasing the concentration of gasses such as carbon dioxide and carbon monoxide which leads to respiratory problems to humans. In this paper, different machine learning algorithms such as Logistic Regression, KNN (K-Nearest Neighbor), Support Vector Machine (SVM), Decision Tree, Naive Bayes, and Random Forest have been used for a study. The predictive accuracy obtained by the algorithms were 56%, 50%, 62%, 56%, 48% and 55% respectively. The SVM model was found to be the best when compared with other machine learning algorithms with a predictive accuracy of 62%.