Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility
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 t...
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| Published in | Geomatics, natural hazards and risk Vol. 14; no. 1 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Abingdon
Taylor & Francis
31.12.2023
Taylor & Francis Ltd Taylor & Francis Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1947-5705 1947-5713 1947-5713 |
| DOI | 10.1080/19475705.2023.2206512 |
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| 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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| AbstractList | 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. AbstractIn 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. |
| Author | Bateni, Sayed M. Pai, Hao-Thing Mosavi, Amir Jun, Changhyun Im, Jungho Janizadeh, Saeid Band, Shahab S. |
| Author_xml | – sequence: 1 givenname: Saeid surname: Janizadeh fullname: Janizadeh, Saeid organization: Department of Civil and Environmental Engineering and Water Resources Research Center, University of HI at Manoa – sequence: 2 givenname: Sayed M. surname: Bateni fullname: Bateni, Sayed M. organization: Department of Civil and Environmental Engineering and Water Resources Research Center, University of HI at Manoa – sequence: 3 givenname: Changhyun surname: Jun fullname: Jun, Changhyun organization: Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University – sequence: 4 givenname: Jungho surname: Im fullname: Im, Jungho organization: Department of Environmental Resources Engineering, State University of NY, College of Environmental Science and Forestry – sequence: 5 givenname: Hao-Thing surname: Pai fullname: Pai, Hao-Thing organization: International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology – sequence: 6 givenname: Shahab S. surname: Band fullname: Band, Shahab S. organization: Future Technology Research Center, College of Future, National Yunlin University of Science and Technology – sequence: 7 givenname: Amir surname: Mosavi fullname: Mosavi, Amir organization: Department of Informatics, J. Selye University |
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| Snippet | In this study, the generalized linear model (GLM) and four ensemble methods (partial least squares (PLS), boosting, bagging, and Bayesian) were applied to... AbstractIn this study, the generalized linear model (GLM) and four ensemble methods (partial least squares (PLS), boosting, bagging, and Bayesian) were applied... |
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| SubjectTerms | Algorithms artificial intelligence Bagging Bayesian analysis Bayesian theory Chalus Rood watershed ensemble Fire hazards forest fire susceptibility Forest fires Forests Generalized linear model Generalized linear models Independent variables Mathematical models mathematics Modelling natural hazards Probability theory Statistical models Vegetation Watersheds |
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| Title | Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility |
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