Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case

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dc.authoridEKER, REMZİ/0000-0002-9322-9634en_US
dc.authorscopusid55303091800en_US
dc.authorscopusid58191222800en_US
dc.authorscopusid36460530900en_US
dc.authorwosidEKER, REMZİ/AAY-3790-2020en_US
dc.contributor.authorEker, Remzi
dc.contributor.authorAlkis, Kamber Can
dc.contributor.authorAydin, Abdurrahim
dc.date.accessioned2024-08-23T16:07:04Z
dc.date.available2024-08-23T16:07:04Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractDisturbances such as forest fires, intense winds, and insect damage exert strong impacts on forest ecosystems by shaping their structure and growth dynamics, with contributions from climate change. Consequently, there is a need for reliable and operational methods to monitor and map these disturbances for the development of suitable management strategies. While susceptibility assessment using machine learning methods has increased, most studies have focused on a single disturbance. Moreover, there has been limited exploration of the use of Automated Machine Learning (AutoML) in the literature. In this study, susceptibility assessment for multiple forest disturbances (fires, insect damage, and wind damage) was conducted using the PyCaret AutoML framework in the Izmir Regional Forest Directorate (RFD) in Turkey. The AutoML framework compared 14 machine learning algorithms and ranked the best models based on AUC (area under the curve) values. The extra tree classifier (ET) algorithm was selected for modeling the susceptibility of each disturbance due to its good performance (AUC values > 0.98). The study evaluated susceptibilities for both individual and multiple disturbances, creating a total of four susceptibility maps using fifteen driving factors in the assessment. According to the results, 82.5% of forested areas in the Izmir RFD are susceptible to multiple disturbances at high and very high levels. Additionally, a potential forest disturbances map was created, revealing that 15.6% of forested areas in the Izmir RFD may experience no damage from the disturbances considered, while 54.2% could face damage from all three disturbances. The SHAP (Shapley Additive exPlanations) methodology was applied to evaluate the importance of features on prediction and the nonlinear relationship between explanatory features and susceptibility to disturbance.en_US
dc.description.sponsorshipIzmir Katip Celebi Universityen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1007/s11676-024-01723-9
dc.identifier.issn1007-662X
dc.identifier.issn1993-0607
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85189202592en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s11676-024-01723-9
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14475
dc.identifier.volume35en_US
dc.identifier.wosWOS:001197259300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherNortheast Forestry Univen_US
dc.relation.ispartofJournal of Forestry Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAutoMLen_US
dc.subjectForest disturbancesen_US
dc.subjectForest fireen_US
dc.subjectInsecten_US
dc.subjectSusceptibilityen_US
dc.subjectWinden_US
dc.subjectClimateen_US
dc.titleAssessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate caseen_US
dc.typeArticleen_US

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