Prediction of forest fire using ensemble method

In this paper we consider the application of ensemble classification method, which is called as the Adaptive Boosting (AdaBoost) method, to predict the occurrences of forest fire. To illustrate the method, we consider the application of the method using the same public data set, which has been used...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-06, Vol.1918 (4), p.42043
Main Authors: Rosadi, D, Andriyani, W.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Clustering
Decision trees
Forest fires
Machine learning
Physics
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Summary:In this paper we consider the application of ensemble classification method, which is called as the Adaptive Boosting (AdaBoost) method, to predict the occurrences of forest fire. To illustrate the method, we consider the application of the method using the same public data set, which has been used in the previous studies, but the ensemble approach is not considered in these studies yet. We also compare the performance of the ensemble method with several other classical classification methods, such as the Decision tree and SVM method. All computation are done using open source software R. We find that in the empirical study, the hybrid algorithms between the fuzzy c-means clustering and the ensemble approach will outperform the other classification methods considered in the study.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1918/4/042043