Predicting potential fire severity in Türkiye's diverse forested areas: a SHAP-integrated random forest classification approach

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dc.authoridEker, Remzi/0000-0002-9322-9634
dc.authoridAYDIN, Abdurrahim/0000-0002-6572-3395
dc.contributor.authorEker, Remzi
dc.contributor.authorAydin, Abdurrahim
dc.date.accessioned2025-10-11T20:48:46Z
dc.date.available2025-10-11T20:48:46Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study introduces a methodology that integrates SHAP (SHapley Additive exPlanations) analysis with Random Forest (RF) classification to enhance the prediction accuracy of fire severity across diverse forested regions in T & uuml;rkiye. Leveraging a comprehensive forest fire database spanning from 2018 to 2022 and utilizing the Google Earth Engine (GEE) platform, 436 fire events ranging from 9 to 53,764.1 hectares were automatically detected and mapped using the difference Normalized Burn Ratio (dNBR). Subsequently, a robust fire severity model was developed by incorporating 19 variables, including biophysical, topographic, climatic, and vegetation-related factors. The RF classification achieved noteworthy performance metrics, with an overall accuracy of 0.75, a Kappa value of 0.61, and a macro-average AUC value of 0.88. Furthermore, the integration of SHAP analysis provided insightful contributions to the RF classification model, elucidating the impacts of individual input features. Notably, variables such as NDMI (Normalized Difference Moisture Index), LAI (Leaf Area Index), and LWVI (Land Water Vegetation Index) emerged as significant influencers, followed by WSPD (Wind Speed) and LST (Land Surface Temperature). Additionally, an analysis of fire severity distribution across fuel types (FMT) revealed intricate patterns, underscoring the complex relationships between vegetation composition and fire behavior. The findings of this study have implications for forest management and wildfire risk assessment, offering valuable insights for decision-making processes. Furthermore, the integration of SHAP analysis with RF classification enhances the interpretability and transparency of machine learning-based fire severity prediction models, contributing to the advancement of fire management strategies.en_US
dc.identifier.doi10.1007/s00477-024-02820-1
dc.identifier.endpage4628en_US
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85204522240en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage4607en_US
dc.identifier.urihttps://doi.org/10.1007/s00477-024-02820-1
dc.identifier.urihttps://hdl.handle.net/20.500.12684/22092
dc.identifier.volume38en_US
dc.identifier.wosWOS:001318543100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofStochastic Environmental Researchand Risk Assessmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectForest fireen_US
dc.subjectFire severityen_US
dc.subjectGoogle Earth Engineen_US
dc.subjectRandom forest classificationen_US
dc.subjectSHAPen_US
dc.titlePredicting potential fire severity in Türkiye's diverse forested areas: a SHAP-integrated random forest classification approachen_US
dc.typeArticleen_US

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