Prediction of Forest Fire Using Machine Learning Algorithms: The Search for the Better Algorithm

Forest fire has several devastating effects on the natural vegetation and the forest lives. The forest fire plays an important role in everyone’s lives and also in our environment. Forest fire is an integral part of many ecosystems such as grassland, temperate forest etc. The ability to predict the area where the forest fire may occur will help in optimizing the situation. The paper presented the prediction of forest fire risk with the help of a machine learning algorithm by using meteorological data. From the existing literature and Limitations, we can show that Different studies have shown the amount of burnt area due to the forest fire, and many have proposed different models to predict forest fire. But there is no such literature which predicts the depth of risk for this forest fire specifically. For that reason, the objective of this work is to predict the risk of forest fire by identifying the particular area as highly prone, moderately prone, low prone and no fire prone area. As a Present Research, in this paper we have worked with different classification models to check which models work best to predict forest fire with greater accuracy. The results we have obtained with the help of various classifiers in machine learning are much better and reliable than the results obtained by traditional computing methods. Thus, this paper indicates a deeper investigation in the field of predicting forest fire risk through machine learning. As a contribution, in this paper we have used SVM, KNN, Decision Tree, Naive Bayes classifier for prediction purposes. The main objective of this paper is to predict the possibility of forest fire with its intensity in specific atmospheric conditions in a given location. We have made comparison of the performance analysis of the different machine learning classifiers. At the end of the abstract, we got the highest AUC value of 0.99 and classification accuracy of 0.98 using Decision Tree to predict the same.