Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey
Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help re...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/3959150 |
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| _version_ | 1832552687009267712 |
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| author | Sohaib K. M. Abujayyab Moustafa Moufid Kassem Ashfak Ahmad Khan Raniyah Wazirali Mücahit Coşkun Enes Taşoğlu Ahmet Öztürk Ferhat Toprak |
| author_facet | Sohaib K. M. Abujayyab Moustafa Moufid Kassem Ashfak Ahmad Khan Raniyah Wazirali Mücahit Coşkun Enes Taşoğlu Ahmet Öztürk Ferhat Toprak |
| author_sort | Sohaib K. M. Abujayyab |
| collection | DOAJ |
| description | Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey. |
| format | Article |
| id | doaj-art-10a290f3a6e14ed88960e41ae43e66f3 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-10a290f3a6e14ed88960e41ae43e66f32025-02-03T05:58:00ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/3959150Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of TurkeySohaib K. M. Abujayyab0Moustafa Moufid Kassem1Ashfak Ahmad Khan2Raniyah Wazirali3Mücahit Coşkun4Enes Taşoğlu5Ahmet Öztürk6Ferhat Toprak7Fire Safety EngineeringSchool of Civil EngineeringDepartment of GeographyCollege of Computing and InformaticsDepartment of GeographyDepartment of GeographyDepartment of GeographyDepartment of GeographyForest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey.http://dx.doi.org/10.1155/2022/3959150 |
| spellingShingle | Sohaib K. M. Abujayyab Moustafa Moufid Kassem Ashfak Ahmad Khan Raniyah Wazirali Mücahit Coşkun Enes Taşoğlu Ahmet Öztürk Ferhat Toprak Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey Advances in Civil Engineering |
| title | Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey |
| title_full | Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey |
| title_fullStr | Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey |
| title_full_unstemmed | Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey |
| title_short | Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey |
| title_sort | wildfire susceptibility mapping using five boosting machine learning algorithms the case study of the mediterranean region of turkey |
| url | http://dx.doi.org/10.1155/2022/3959150 |
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