Capitalizing the predictive potential of machine learning to detect various fire types using NASA's MODIS satellite data for the mediterranean basin

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buir.contributor.authorLassem, Nima Kamali
buir.contributor.authorAli, Seyid Amjad
buir.contributor.orcidLassem, Nima Kamali|0009-0008-4326-9639
buir.contributor.orcidAli, Seyid Amjad|0000-0001-9250-9020
dc.citation.epage28en_US
dc.citation.spage24
dc.contributor.authorLassem, Nima Kamali
dc.contributor.authorGaafar, Obai Mohamed Hisham Abdelmohsen
dc.contributor.authorAli, Seyid Amjad
dc.coverage.spatialIstanbul, Turkiye
dc.date.accessioned2024-03-07T13:41:34Z
dc.date.available2024-03-07T13:41:34Z
dc.date.issued2024-01-22
dc.departmentComputer Technology and Information Systems
dc.descriptionConference Name: ICAAI '23: Proceedings of the 2023 7th International Conference on Advances in Artificial Intelligence
dc.descriptionDate of Conference: 13 - 15 October 2023
dc.description.abstractThis study investigates the realm of machine learning for the classification of different fire types using NASA's FIRMS MODIS satellite data for the Mediterranean basin. Concentrating on the Mediterranean basin and utilizing data spanning from 2019 to 2021 for model training, XGBoost and Random Forest models were subsequently validated for the 2022 data. The findings distinctly illustrate XGBoost's superior predictive precision as compared to Random Forest by showcasing an impressive overall F1 score surpassing 95% and 84% macro F1 score across various fire types. This study emphasizes the prospect of machine learning to improve worldwide wildfire monitoring and response by providing exact, real-time fire type forecasts.
dc.description.provenanceMade available in DSpace on 2024-03-07T13:41:34Z (GMT). No. of bitstreams: 1 Capitalizing_the_predictive_potential_of_machine_learning_to_detect_various_fire_types_using_NASA's_MODIS_satellite_data_for_the_mediterranean_basin.pdf: 751927 bytes, checksum: 6f29cceea700c227032ffc98a57a535f (MD5) Previous issue date: 2024-01-22en
dc.identifier.doi10.1145/3633598.3633603
dc.identifier.isbn9798400708985
dc.identifier.urihttps://hdl.handle.net/11693/114393
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.isversionofhttps://doi.org/10.1145/3633598.3633603
dc.source.titleACM International Conference Proceeding Series
dc.subjectWildfire prediction
dc.subjectMODIS
dc.subjectMediterranean basin
dc.subjectXGBoost
dc.subjectRandom Forest
dc.titleCapitalizing the predictive potential of machine learning to detect various fire types using NASA's MODIS satellite data for the mediterranean basin
dc.typeConference Paper

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