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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/169298

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Title: Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)
Author: Jimeno-Sáez, Patricia Senent-Aparicio, Javier Cecilia-Canales, José María Pérez-Sánchez, Julio
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Issued date:
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 ...[+]
Subjects: Multilayer neural network (MLNN) , Support vector regression (SVR) , Water quality , Eutrophication , Chlorophyll-a , Mar Menor coastal lagoon
Copyrigths: Reconocimiento (by)
Source:
International Journal of Environmental research and Public Health (Online). (eissn: 1660-4601 )
DOI: 10.3390/ijerph17041189
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/ijerph17041189
Project ID:
info:eu-repo/grantAgreement/f SéNeCa//20813%2FPI%2F18/
info:eu-repo/grantAgreement/AEI//RTC-2017-6389-5/
info:eu-repo/grantAgreement/AEI//RYC-2018-025580-I/
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/
Thanks:
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 ...[+]
Type: Artículo

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