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Article

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

1
RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
2
Department of Surveying and Geoinformatics, Faculty of Environmental Sciences, University of Ilorin, P.M.B. 1515, 240103 Ilorin, Nigeria
3
Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran P.O. Box 14115-111, Iran
4
Department of Forestry, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran 15119-43943, Iran
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Department of Biological Sciences, Global Ecology and ARC Centre of Excellence for Australian Biodiversity and Heritage, College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia
6
Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(22), 3682; https://doi.org/10.3390/rs12223682
Received: 9 October 2020 / Revised: 5 November 2020 / Accepted: 5 November 2020 / Published: 10 November 2020
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 vegetation indices, climatic variables, environmental factors, and topographical features. To assess the suitability of the models and estimating the variance and bias of estimation, the training dataset obtained from the Natural Resources Directorate of Mazandaran province was subjected to resampling using cross validation (CV), bootstrap, and optimism bootstrap techniques. Using variance inflation factor (VIF), weight indicating the strength of the spatial relationship of the predictors to fire occurrence was assigned to each contributing variable. Subsequently, the models were trained and validated using the receiver operating characteristics (ROC) area under the curve (AUC) curve. Results of the model validation based on the resampling techniques (non, 5- and 10-fold CV, bootstrap and optimism bootstrap) produced AUC values of 0.78, 0.88, 0.90, 0.86 and 0.83 for the MARS model; 0.82, 0.82, 0.89, 0.87, 0.84 for the SVM and 0.87, 0.90, 0.90, 0.90, 0.91 for the BRT model. Across the individual model, the 10-fold CV performed best in MARS and SVM with AUC values of 0.90 and 0.89. Overall, the BRT outperformed the other models in all ramification with highest AUC value of 0.91 using optimism bootstrap resampling algorithm. Generally, the resampling process enhanced the prediction performance of all the models. View Full-Text
Keywords: machine learning; remote sensing; computational intelligence; bootstrapping; cross validation (CV) machine learning; remote sensing; computational intelligence; bootstrapping; cross validation (CV)
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MDPI and ACS Style

Kalantar, B.; Ueda, N.; Idrees, M.O.; Janizadeh, S.; Ahmadi, K.; Shabani, F. Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data. Remote Sens. 2020, 12, 3682. https://doi.org/10.3390/rs12223682

AMA Style

Kalantar B, Ueda N, Idrees MO, Janizadeh S, Ahmadi K, Shabani F. Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data. Remote Sensing. 2020; 12(22):3682. https://doi.org/10.3390/rs12223682

Chicago/Turabian Style

Kalantar, Bahareh, Naonori Ueda, Mohammed O. Idrees, Saeid Janizadeh, Kourosh Ahmadi, and Farzin Shabani. 2020. "Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data" Remote Sensing 12, no. 22: 3682. https://doi.org/10.3390/rs12223682

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