Predicting Forest Fires using Supervised and Ensemble Machine Learning Algorithms
R. Rishickesh1, A. Shahina2, A. Nayeemulla Khan3 

1R. Rishickesh, Department of Information Technology, SSN College of Engineering, Kalavakkam-603110, India.
2A. Shahina, Department of Information Technology, SSN College of Engineering, Kalavakkam-603110, India.
3A. Nayeemulla Khan, School of Computing Science and Engineering, VIT University, Chennai-600127, India.

Manuscript received on 05 March 2019 | Revised Manuscript received on 11 March 2019 | Manuscript published on 30 July 2019 | PP: 3697-3705 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2878078219/19©BEIESP | DOI: 10.35940/ijrte.B2878.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Forest fires have become one of the most frequently occurring disasters in recent years. The effects of forest fires have a lasting impact on the environment as it lead to deforestation and global warming, which is also one of its major cause of occurrence. Forest fires are dealt by collecting the satellite images of forest and if there is any emergency caused by the fires then the authorities are notified to mitigate its effects. By the time the authorities get to know about it, the fires would have already caused a lot of damage. Data mining and machine learning techniques can provide an efficient prevention approach where data associated with forests can be used for predicting the eventuality of forest fires. This paper uses the dataset present in the UCI machine learning repository which consists of physical factors and climatic conditions of the Montesinho park situated in Portugal. Various algorithms like Logistic regression, Support Vector Machine, Random forest, K-Nearest neighbors in addition to Bagging and Boosting predictors are used, both with and without Principal Component Analysis (PCA). Among the models in which PCA was applied, Logistic Regression gave the highest F-1 score of 68.26 and among the models where PCA was absent, Gradient boosting gave the highest score of 68.36.
Index Terms: Forest Fires, Principal Component Analysis, Supervised Learning Algorithms, Ensemble Learning Algorithms.

Scope of the Article: E-Learning