@prefix adms: . @prefix cnt: . @prefix dcat: . @prefix dcatap: . @prefix dct: . @prefix eli: . @prefix foaf: . @prefix gsp: . @prefix locn: . @prefix skos: . @prefix vcard: . @prefix xsd: . a dcat:Dataset ; dcatap:applicableLegislation ; dcatap:hvdCategory ; dct:accessRights ; dct:conformsTo ; dct:creator [ a foaf:Agent ; dct:identifier "E05068001" ; dct:type ; foaf:mbox ; foaf:name "González-Enrique, J., Ruiz-Aguilar, J.J.,…"@en ] ; dct:description """The goal of this research is to develop accurate and reliable forecasting models for chlorophyll ɑ concentrations in seawater at multiple depth levels in El Mar Menor (Spain). Chlorophyll ɑ can be used as a eutrophication indicator, which is especially essential in a rich yet vulnerable ecosystem like the study area. Bayesian regularized artificial neural networks and Long Short-term Memory Neural Networks (LSTMs) employing a rolling window approach were used as forecasting algorithms with a one-week prediction horizon. Two input strategies were tested: using data from the own time series or including exogenous variables among the inputs. In this second case, mutual information and the Minimum-Redundancy-Maximum-Relevance approach were utilized to select the most relevant variables. The models obtained reasonable results for the univariate input scheme with σ¯¯¯ values over 0.75 in levels between 0.5 and 2 m. The inclusion of exogenous variables increased these values to above 0.85 for the same depth levels. The models and methodologies presented in this paper can constitute a very useful tool to help predict eutrophication episodes and act as decision-making tools that allow the governmental and environmental agencies to prevent the degradation of El Mar Menor."""@en, """The goal of this research is to develop accurate and reliable forecasting models for chlorophyll ɑ concentrations in seawater at multiple depth levels in El Mar Menor (Spain). Chlorophyll ɑ can be used as a eutrophication indicator, which is especially essential in a rich yet vulnerable ecosystem like the study area. Bayesian regularized artificial neural networks and Long Short-term Memory Neural Networks (LSTMs) employing a rolling window approach were used as forecasting algorithms with a one-week prediction horizon. Two input strategies were tested: using data from the own time series or including exogenous variables among the inputs. In this second case, mutual information and the Minimum-Redundancy-Maximum-Relevance approach were utilized to select the most relevant variables. The models obtained reasonable results for the univariate input scheme with σ¯¯¯ values over 0.75 in levels between 0.5 and 2 m. The inclusion of exogenous variables increased these values to above 0.85 for the same depth levels. The models and methodologies presented in this paper can constitute a very useful tool to help predict eutrophication episodes and act as decision-making tools that allow the governmental and environmental agencies to prevent the degradation of El Mar Menor."""@es ; dct:identifier "06ca0bca-0592-5264-b443-8a149b7651c1" ; dct:issued "2024-11-05T00:00:00+00:00"^^xsd:dateTime ; dct:language ; dct:license ; dct:modified "2026-06-25T00:00:00+00:00"^^xsd:dateTime ; dct:publisher ; dct:spatial ; dct:title "Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain)"@en, "Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain)"@es ; dct:type ; adms:identifier "DOI: 10.1007/978-3-031-18050-7_8" ; adms:status ; dcat:contactPoint ; dcat:distribution ; dcat:keyword "aguas_interiores"@en ; dcat:landingPage ; dcat:theme , . a dct:Location ; skos:prefLabel "Región de Murcia" ; dcat:bbox "POLYGON ((-2.3400 37.3800, -0.6900 37.3800, -0.6900 38.7600, -2.3400 38.7600, -2.3400 37.3800))"^^gsp:wktLiteral ; dcat:centroid "POINT (-1.5150 38.0700)"^^gsp:wktLiteral ; locn:geometry "POLYGON ((-2.3400 37.3800, -0.6900 37.3800, -0.6900 38.7600, -2.3400 38.7600, -2.3400 37.3800))"^^gsp:wktLiteral . a dct:LinguisticSystem . a dct:LinguisticSystem . a foaf:Agent ; dct:type ; vcard:hasEmail ; foaf:homepage ; foaf:name "Área de Banco de Datos de la Naturaleza. Dirección General Biodiversidad, Bosques y Desertificación. Ministerio para la Transición Ecológica y el Reto Demográfico"@en . a foaf:Document . a dcat:Distribution ; dcatap:applicableLegislation ; dcatap:availability ; dct:accessRights ; dct:format ; dct:issued "2023-01-01T00:00:00+00:00"^^xsd:dateTime ; dct:language ; dct:license ; dct:modified "2026-06-25T00:00:00+00:00"^^xsd:dateTime ; dct:title "Distribución HTML"@en, "Distribución HTML"@es ; cnt:characterEncoding "UTF-8" ; dcat:accessURL ; dcat:mediaType . a vcard:Kind ; vcard:fn "Organismo publicador del Catálogo"@en ; vcard:hasEmail ; vcard:hasURL ; vcard:role . a dct:Standard . a eli:LegalResource . dct:type .