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Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibilityopen access

Authors
Janizadeh, SaeidBateni, Sayed M.Jun, ChanghyunIm, JunghoPai, Hao-ThingBand, Shahab S.Mosavi, Amir
Issue Date
Dec-2023
Publisher
TAYLOR & FRANCIS LTD
Keywords
Generalized linear model; ensemble; natural hazards; Chalus Rood watershed; forest fire susceptibility; artificial intelligence; mathematics
Citation
GEOMATICS NATURAL HAZARDS & RISK, v.14, no.1
Journal Title
GEOMATICS NATURAL HAZARDS & RISK
Volume
14
Number
1
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/69681
DOI
10.1080/19475705.2023.2206512
ISSN
1947-5705
1947-5713
Abstract
In this study, the generalized linear model (GLM) and four ensemble methods (partial least squares (PLS), boosting, bagging, and Bayesian) were applied to predict forest fire hazard in the Chalus Rood watershed in the Mazandaran Province, Iran. Data from 108 historical forest fire events collected through field surveys were applied as the basis of the analysis. About 70% of the data were used for training the models, while the remaining 30% was used for testing. A total of 14 environmental, climatic, and vegetation variables were used as input features to the models to predict forest fire probability. After conducting a multicollinearity test on the independent variables, the GLM and the ensemble models were applied for modeling. The efficiency of the models was evaluated using receiver operating characteristic (ROC) curve parameters. Results from the validation process, based on the area under the ROC curve (AUC), showed that the GLM, PLS-GLM, boosted-GLM, Bagging-GLM, and Bayesian-GLM models had efficiencies of 0.79, 0.75, 0.81, 0.84, and 0.85, respectively. The results indicated that all ensemble methods, except the PLS algorithm, improved the performance of the GLM model in modeling forest fire hazards in the Chalus Rood watershed, with the Bayesian algorithm being the most efficient method among them.
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Jun, Changhyun
공과대학 (건설환경플랜트공학)
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