Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

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dc.authoridİsmail Gültepe / 0000-0002-8433-5953
dc.authorscopusidİsmail Gülltepe / 56000281400
dc.authorwosidİsmail Gültepe / CSK-8095-2022
dc.contributor.authorCordeiro, Fabricio Magalhães
dc.contributor.authorFranca, Gutemberg Borges
dc.contributor.authorNeto, F.L.A.
dc.contributor.authorGültepe, İsmail
dc.date.accessioned2022-01-18T13:18:00Z
dc.date.available2022-01-18T13:18:00Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis work presents a novel approach for simulating visibility (Vis) and ceiling base height (Hc ) in up to 1 h using several machine learning (ML) algorithms. Ten years of meteorological data at 15 min intervals for Santos Dumont airport (SDA), Rio de Janeiro, Brazil were used in the ML method training and testing process. In the investigation, several categorical and regressive algorithms were trained and tested, and the results were verified with observations. The forecast results reveal that the categorical methods produced satisfactory results only up to 15 min for visibility prediction with the probability of detection greater than 85%. On the other hand, the regressive methods were found to be more capable of generating an accurate prediction of Vis and Hc compared to categorical method up to 60 min. The forecast evaluation metrics for Vis and Hc had correlation coefficients of 0.99 ± 0.00 and 0.96 ± 0.00, with mean absolute errors of 324 ± 77 m, and 167 ± 21 m, respectively. Results suggested that ML methods can improve the prediction of Vis and Hc up to 1 h when accurate observations are used for the analysis. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.identifier.citationCordeiro, F. M., França, G. B., Neto, F. L. A., & Gultepe, I. (2021). Visibility and ceiling nowcasting using artificial intelligence techniques for aviation applications. Atmosphere, 12(12)en_US
dc.identifier.doi10.3390/atmos12121657en_US
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85121964589en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.3390/atmos12121657
dc.identifier.uri2073-4433
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2400
dc.identifier.volume12en_US
dc.identifier.wosWOS:000735438700001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGültepe, İsmail
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofAtmosphereen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAeronautical Meteorologyen_US
dc.subjectAviation Meteorologyen_US
dc.subjectCeilingen_US
dc.subjectNowcastingen_US
dc.subjectVisibilityen_US
dc.subjectWeather Predictionen_US
dc.titleVisibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applicationsen_US
dc.typeArticleen_US

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