Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones

Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones

This study aimed to enhance the accuracy of forest fire susceptibility mapping (FSM) by innovatively applying recursive feature elimination (RFE) with an ensemble of machine learning models, specifically Support Vector Machine (SVM) and Random Forest (RF), to identify key fire factors. The fire zone...

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Bibliographic Details
Main Authors: Ali Rezaei Barzani, Parham Pahlavani, Omid Ghorbanzadeh, Khalil Gholamnia, Pedram Ghamisi
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Fire
Subjects:
AdaBoost
Extra trees
Gradient Boosting
RF
SVM
forest fire susceptibility map (FSM)
Online Access:https://www.mdpi.com/2571-6255/7/12/440

Internet

https://www.mdpi.com/2571-6255/7/12/440

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