A Deep Learning Approach for Wave Forecasting Based on a Spatially Correlated Wind Feature, with a Case Study in the Java Sea, Indonesia

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datacite.relatedItem.firstPage39
datacite.relatedItem.issue1
datacite.relatedItem.relatedIdentifierTypeISSN
datacite.relatedItem.relatedItemIdentifier2311-5521
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datacite.relatedItem.titleFluids
datacite.relatedItem.volume7
dc.contributor.authorDidit Adytia
dc.contributor.authorDeni Saepudin
dc.contributor.authorSri Redjeki Pudjaprasetya
dc.contributor.authorSemeidi Husrin
dc.contributor.authorArdhasena Sopaheluwakan
dc.date.accessioned2025-04-16T11:37:53Z
dc.date.available2025-04-16T11:37:53Z
dc.date.issued2022
dc.identifier.doihttps://doi.org/10.3390/fluids7010039
dc.identifier.otherjz000102-0378
dc.identifier.urihttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/15088
dc.publisherMDPI AG
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc530
dc.titleA Deep Learning Approach for Wave Forecasting Based on a Spatially Correlated Wind Feature, with a Case Study in the Java Sea, Indonesia
dc.typeArticle
dcat.distribution.pdfhttps://tustorage.ulb.tu-darmstadt.de/handle/tustorage/15089
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