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dc.contributor.authorZhao, W. P.
dc.contributor.authorLi, J.
dc.contributor.authorZhao, J.
dc.contributor.authorZhao, D.
dc.contributor.authorLu, J.
dc.contributor.authorWang, X.
dc.date.accessioned2020-05-04T09:39:03Z
dc.date.available2020-05-04T09:39:03Z
dc.date.issued2020-04cs
dc.identifier.citationRadioengineering. 2020 vol. 29, č. 1, s. 81-93. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/186937
dc.description.abstractEvaporation duct is a specific atmospheric structure at sea, which has an important influence on the propagation path of electromagnetic waves (EW). Considering the limit of existing evaporation duct height (EDH) prediction models and aiming at prpoposing more accurate and stronger generalization ability of EDH models, we applied eXtreme Gradient Boosting (XGBoosting) algorithm to the field of evaporation duct for the first time. And we proposed the new EDH prediction model using XGBoost algorithm(XGB model). Simultaneously, traditional Paulus-Jeske (PJ) model and deep learning Multilayer Perceptron (MLP) model were introduced into the experiment to make a comparison. In terms of comprehensive performance, XGB model is optimal in all sub-regions and total area. Finally, cross-learning experiments were carried out to test the generalization ability of XGB model. The results show that the generalization ability of XGB model is better than that of MLP model.en
dc.formattextcs
dc.format.extent81-93cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2019/20_01_0081_0093.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectEvaporation ducten
dc.subjectmachine learningen
dc.subjectXGBoost algorithmen
dc.subjectXGB modelen
dc.subjectPaulus-Jeske (PJ) modelen
dc.titleXGB Model : Research on Evaporation Duct Height Prediction Based on XGBoost Algorithmen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue1cs
dc.coverage.volume29cs
dc.identifier.doi10.13164/re.2020.0081en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution 4.0 International license
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International license