@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 "Jimeno-Saez, P., Senent-Aparicio, J., Cecilia, J.M. y Perez-Sanchez, J."@en ] ; dct:description "The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R-CV(2) (cross-validated coefficient of determination) for the best-fit models."@en, "The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R-CV(2) (cross-validated coefficient of determination) for the best-fit models."@es ; dct:identifier "fdc45613-002f-5f83-8c90-0402d42957f5" ; 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 "Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)"@en, "Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)"@es ; dct:type ; adms:identifier "DOI: 10.3390/ijerph17041189" ; 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 "2020-02-13T00:00:00+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 .