A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area

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dc.authoridhttps://orcid.org/0000-0002-4388-1480en_US
dc.authoridhttps://orcid.org/0000-0002-2881-8635en_US
dc.authoridhttps://orcid.org/0000-0001-8579-5444en_US
dc.authoridhttps://orcid.org/0000-0002-0418-4092en_US
dc.contributor.authorDeif, Mohanad A.
dc.contributor.authorSolyman, Ahmad Amin Ahmad
dc.contributor.authorAlsharif, Mohammed H.
dc.contributor.authorJung, Seungwon
dc.contributor.authorHwang, Eenjun
dc.date.accessioned2023-10-04T11:45:29Z
dc.date.available2023-10-04T11:45:29Z
dc.date.issued2022en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractTemperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.en_US
dc.identifier.doi10.3390/su14010296en_US
dc.identifier.endpage17en_US
dc.identifier.issn2071-1050
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85122830832en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/11363/5744
dc.identifier.urihttps://doi.org/
dc.identifier.volume14en_US
dc.identifier.wosWOS:000743127100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSolyman, Ahmad Amin Ahmad
dc.language.isoenen_US
dc.publisherMDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLANDen_US
dc.relation.ispartofSustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectsupport vector machine (SVM)en_US
dc.subjectgray wolf optimizer (GWO)en_US
dc.subjecttemperature forecastingen_US
dc.subjectnumerical weather prediction (NWP) modelen_US
dc.titleA Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Areaen_US
dc.typeArticleen_US

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