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dc.contributor.authorGómez-Orellana, Antonio Manuel
dc.contributor.authorFernández, Juan Carlos
dc.contributor.authorDorado Moreno, Manuel
dc.contributor.authorGutiérrez, Pedro A.
dc.contributor.authorHervás-Martínez, César
dc.date.accessioned2021-01-18T10:34:28Z
dc.date.available2021-01-18T10:34:28Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/10396/20980
dc.description.abstractMeteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented.es_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceEnergies 14(2), 468 (2021)es_ES
dc.subjectEnvironmental predictiones_ES
dc.subjectRenewable energy resource evaluationes_ES
dc.subjectMeteorological dataes_ES
dc.subjectReanalysis dataes_ES
dc.subjectMarine energyes_ES
dc.subjectSoft computinges_ES
dc.titleBuilding Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Fluxes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/en14020468es_ES
dc.relation.projectIDGobierno de España. TIN2017-85887-C2-1-Pes_ES
dc.relation.projectIDJunta de Andalucía. UCO-1261651es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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