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Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

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Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

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dc.contributor.author Jimeno-Sáez, Patricia es_ES
dc.contributor.author Senent-Aparicio, Javier es_ES
dc.contributor.author Cecilia-Canales, José María es_ES
dc.contributor.author Pérez-Sánchez, Julio es_ES
dc.date.accessioned 2021-07-15T03:36:24Z
dc.date.available 2021-07-15T03:36:24Z
dc.date.issued 2020-02 es_ES
dc.identifier.uri http://hdl.handle.net/10251/169298
dc.description.abstract [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_ES
dc.description.sponsorship This research was partially funded by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 and RTC-2017-6389-5. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof International Journal of Environmental research and Public Health (Online) es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Multilayer neural network (MLNN) es_ES
dc.subject Support vector regression (SVR) es_ES
dc.subject Water quality es_ES
dc.subject Eutrophication es_ES
dc.subject Chlorophyll-a es_ES
dc.subject Mar Menor coastal lagoon es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain) es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/ijerph17041189 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RTC-2017-6389-5/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RYC-2018-025580-I/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096384-B-I00/ES/SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Jimeno-Sáez, P.; Senent-Aparicio, J.; Cecilia-Canales, JM.; Pérez-Sánchez, J. (2020). Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental research and Public Health (Online). 17(4):1-14. https://doi.org/10.3390/ijerph17041189 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/ijerph17041189 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 17 es_ES
dc.description.issue 4 es_ES
dc.identifier.eissn 1660-4601 es_ES
dc.identifier.pmid 32069834 es_ES
dc.identifier.pmcid PMC7068380 es_ES
dc.relation.pasarela S\403848 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Fundación Séneca-Agencia de Ciencia y Tecnología de la Región de Murcia es_ES
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