@prefix adms: <http://www.w3.org/ns/adms#> .
@prefix cnt: <http://www.w3.org/2011/content#> .
@prefix dc: <http://purl.org/dc/elements/1.1/> .
@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix gsp: <http://www.opengis.net/ont/geosparql#> .
@prefix locn: <http://www.w3.org/ns/locn#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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            foaf:mbox <mailto:javier.gonzalezenrique@uca.es> ;
            foaf:name "González-Enrique, J., Ruiz-Aguilar, J.J.,…"@es ] ;
    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."""@es ;
    dct:identifier "https://pro.iepnb.gob.es/catalogo/dataset/06ca0bca-0592-5264-b443-8a149b7651c1" ;
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    dct:spatial <http://datos.gob.es/recurso/sector-publico/territorio/Autonomia/Region-Murcia> ;
    dct:title "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 <http://id.loc.gov/vocabulary/marcgt/art> ;
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<https://pro.iepnb.gob.es/catalogo/dataset/06ca0bca-0592-5264-b443-8a149b7651c1/resource/ecd85ede-f550-4b28-93ac-c622a1b35b3d> a dcat:Distribution ;
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    dct:format [ a dct:IMT ;
            rdfs:label "HTML" ;
            rdf:value "text/html" ] ;
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    dct:modified "2026-06-25T00:00:00+00:00"^^xsd:dateTime ;
    dct:title "Distribución HTML"@es ;
    cnt:characterEncoding "UTF-8" ;
    dcat:accessURL <https://books.google.com/books?hl=en&lr=&id=b_WVEAAAQBAJ&oi=fnd&pg=PA72&dq=mar+menor+lagoon&ots=Ggta7vMKzu&sig=093_PZ_uh7sIqBo3MaL0rNqUTuE> .

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