Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

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dc.authoridTOPRAK, FERHAT/0000-0001-5452-5855
dc.authoridWazirali, Raniyah/0000-0002-3609-9351
dc.authoridKASSEM, MOUSTAFA/0000-0003-2707-685X
dc.authoridBeddu, Salmia/0000-0001-9451-0690
dc.authoridK. M. Abujayyab, Sohaib/0000-0002-6692-3567
dc.authoridTASOGLU, Enes/0000-0002-6365-6926
dc.contributor.authorAbujayyab, Sohaib K. M.
dc.contributor.authorKassem, Moustafa Moufid
dc.contributor.authorKhan, Ashfak Ahmad
dc.contributor.authorWazirali, Raniyah
dc.contributor.authorOzturk, Ahmet
dc.contributor.authorToprak, Ferhat
dc.date.accessioned2024-09-29T16:04:53Z
dc.date.available2024-09-29T16:04:53Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractForest 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.en_US
dc.identifier.doi10.1155/2022/3959150
dc.identifier.issn1687-8086
dc.identifier.issn1687-8094
dc.identifier.scopus2-s2.0-85145877472en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1155/2022/3959150
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6382
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000908909000001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofAdvances in Civil Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDriving Factorsen_US
dc.subjectLandslide Susceptibilityen_US
dc.subjectLogistic-Regressionen_US
dc.subjectDecision Treeen_US
dc.subjectFireen_US
dc.subjectForesten_US
dc.subjectRisken_US
dc.subjectLandscapesen_US
dc.subjectGisen_US
dc.subjectMulticriteriaen_US
dc.titleWildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkeyen_US
dc.typeArticleen_US

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