Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data

Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data

This study predicts forest fire susceptibility in Chaloos Rood watershed in Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector machine (SVM), and boosted regression tree (BRT). The study utilizes 14 set of fire predictors derived from vegeta...

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Bibliographic Details
Main Authors: Bahareh Kalantar, Naonori Ueda, Mohammed O. Idrees, Saeid Janizadeh, Kourosh Ahmadi, Farzin Shabani
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Remote Sensing
Subjects:
machine learning
remote sensing
computational intelligence
bootstrapping
cross validation (CV)
Online Access:https://www.mdpi.com/2072-4292/12/22/3682

Internet

https://www.mdpi.com/2072-4292/12/22/3682

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