Integrating ensemble machine learning and explainable AI for enhanced forest fire susceptibility analysis and risk assessment in Türkiye’s Mediterranean region
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12145-024-01480-7.pdf
Reference104 articles.
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3. Abdollahi A, Pradhan B (2023) Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model. Sci Total Environ 879:163004. https://doi.org/10.1016/j.scitotenv.2023.163004
4. Abedi Gheshlaghi H, Feizizadeh B, Blaschke T et al (2021) Forest fire susceptibility modeling using hybrid approaches. Trans GIS 25:311–333. https://doi.org/10.1111/tgis.12688
5. Abujayyab SKM, Kassem MM, Khan AA et al (2022) Wildfire susceptibility mapping using five boosting machine learning algorithms: the Case Study of the Mediterranean Region of Turkey. Adv Civ Eng 959150:1–18. https://doi.org/10.1155/2022/3959150
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