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Open AccessArticle

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

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University of Transport Technology, Hanoi 100000, Vietnam
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Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran 64414-356, Iran
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Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran 14115-111, Iran
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Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden
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Department of Land Management, School of Agriculture and Resources, Vinh University, Nghe An 43000, Vietnam
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Department of Geography, School of Social Education Vinh University, Nghe An 43000, Vietnam
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Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam
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Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Ha Noi 100000, Vietnam
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department of Forest Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran
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Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India
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Department of Resource and Environment Management, School of Agriculture and Resources, Vinh University, Nghe An 43000, Vietnam
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Authors to whom correspondence should be addressed.
Symmetry 2020, 12(6), 1022; https://doi.org/10.3390/sym12061022
Received: 6 May 2020 / Revised: 12 June 2020 / Accepted: 12 June 2020 / Published: 17 June 2020
Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources. View Full-Text
Keywords: Bayes network; decision tree; multivariate logistic regression; Naïve Bayes; spatial modeling Bayes network; decision tree; multivariate logistic regression; Naïve Bayes; spatial modeling
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Pham, B.T.; Jaafari, A.; Avand, M.; Al-Ansari, N.; Dinh Du, T.; Yen, H.P.H.; Phong, T.V.; Nguyen, D.H.; Le, H.V.; Mafi-Gholami, D.; Prakash, I.; Thi Thuy, H.; Tuyen, T.T. Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction. Symmetry 2020, 12, 1022.

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