Prediction Model for Forests Fire Spread in M’sila
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Date
2023-06
Journal Title
Journal ISSN
Volume Title
Publisher
UNIVERSITY OF MOHAMED BOUDIAF - MSILA
Abstract
In this study, three machine-learning algorithms (Linear regression, Polynomial Regression, and
Random Forest Regression) were explored for predicting forest fires spread. A comprehensive
dataset consisting of environmental and weather factors influencing forest fires was collected and
used to train and test the models. Performance metrics such as accuracy, precision and recall
score were used to evaluate the models. The results showed that all three algorithms performed
well, but the Polynomial Regression Model achieved the highest accuracy. This study
emphasizes the effectiveness of machine learning in forest fire spread prediction, particularly the
superiority of the Polynomial Regression Model, and highlights the importance of leveraging
advanced techniques for mitigating the impact of forest fires and protecting ecosystems
Description
Keywords
Linear regression, Polynomial Regression, Random Forest Regression