@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 "Garcia del Toro, E.M., Francisco Mateo, L., Garcia-Salgado, S., Isabel Mas-Lopez, M. y Angeles Quijano, M."@en ] ; dct:description "The Mar Menor is a Mediterranean coastal saltwater lagoon (Murcia, Spain) that represents a unique ecosystem of vital importance for the area, from both an economic and ecological point of view. During the last decades, the intense agricultural activity has caused episodes of eutrophication due to the contribution of inorganic nutrients, especially nitrates. For this reason, it is important to control the quality of the water discharged into the Mar Menor lagoon, which can be performed through the measurement of dissolved oxygen (DO). Therefore, this article aimed to predict the DO in the water discharged into this lagoon through the El Albujon watercourse, for which two theoretical models consisting of a multiple linear regression (MLR) and a back-propagation neural network (RPROP) were developed. Data of temperature, pH, nitrates, chlorides, sulphates, electrical conductivity, phosphates and DO at the mouth of this watercourse, between January 2014 and January 2021, were used. A preliminary statistical study was performed to discard the variables with the lowest influence on DO. Finally, both theoretical models were compared by means of the coefficient of determination (R-2), the root mean square errors (RMSE) and the mean absolute error (MAE), concluding that the neural network made a more accurate prediction of DO."@en, "The Mar Menor is a Mediterranean coastal saltwater lagoon (Murcia, Spain) that represents a unique ecosystem of vital importance for the area, from both an economic and ecological point of view. During the last decades, the intense agricultural activity has caused episodes of eutrophication due to the contribution of inorganic nutrients, especially nitrates. For this reason, it is important to control the quality of the water discharged into the Mar Menor lagoon, which can be performed through the measurement of dissolved oxygen (DO). Therefore, this article aimed to predict the DO in the water discharged into this lagoon through the El Albujon watercourse, for which two theoretical models consisting of a multiple linear regression (MLR) and a back-propagation neural network (RPROP) were developed. Data of temperature, pH, nitrates, chlorides, sulphates, electrical conductivity, phosphates and DO at the mouth of this watercourse, between January 2014 and January 2021, were used. A preliminary statistical study was performed to discard the variables with the lowest influence on DO. Finally, both theoretical models were compared by means of the coefficient of determination (R-2), the root mean square errors (RMSE) and the mean absolute error (MAE), concluding that the neural network made a more accurate prediction of DO."@es ; dct:identifier "21a5f10f-1a8f-577d-b2fe-58d73522632e" ; 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 "Use of Artificial Neural Networks as a Predictive Tool of Dissolved Oxygen Present in Surface Water Discharged in the Coastal Lagoon of the Mar Menor (Murcia, Spain)"@en, "Use of Artificial Neural Networks as a Predictive Tool of Dissolved Oxygen Present in Surface Water Discharged in the Coastal Lagoon of the Mar Menor (Murcia, Spain)"@es ; dct:type ; adms:identifier [ a adms:Identifier ; skos:notation "DOI: 10.3390/ijerph19084531" ] ; adms:status ; dcat:contactPoint ; dcat:distribution ; dcat:keyword "aguas_exteriores"@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 "2022-04-09T00:00:00+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 .