Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 57, Issue -, Pages 192-201Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2014.03.003
Keywords
Machine learning; Model; Wildfire; Random Forest; Boosted Regression Tree; Support Vector Machine
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Funding
- National I+D Plan of the Spanish Ministry of Science and Innovation: FPI grant [BES-2009-023728]
- FIREGLOBE [CGL2008-01083/CLI]
- PYRORAMA Modeling future scenarios of fire risk and land use changes at national scale [CGL2011-29619-C03-01]
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This paper provides insight into the use of Machine Learning (ML) models for the assessment of human-caused wildfire occurrence. It proposes the use of ML within the context of fire risk prediction, and more specifically, in the evaluation of human-induced wildfires in Spain. In this context, three ML algorithms Random Forest (RF), Boosting Regression Trees (BRT), and Support Vector Machines (SVM)-are implemented and compared with traditional methods like Logistic Regression (LR). Results suggest that the use of any of these ML algorithms leads to an improvement in the accuracy-in terms of the AUC (area under the curve) of the model when compared to LR outputs. According to the AUC values, RF and BRT seem to be the most adequate methods, reaching AUC values of 0.746 and 0.730 respectively. On the other hand, despite the fact that the SVM yields an AUC value higher than that from LR, the authors consider it inadequate for classifying wildfire occurrences because its calibration is extremely timeconsuming. (c) 2014 Elsevier Ltd. All rights reserved.
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