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

Patricia Jimeno-Sáez, Javier Senent-Aparicio, José M. Cecilia and Julio Pérez-Sánchez
Additional contact information
Patricia Jimeno-Sáez: Department of Civil Engineering, Universidad Católica San Antonio de Murcia, Campus de los Jerónimos s/n, 30107 Guadalupe, Murcia, Spain
Javier Senent-Aparicio: Department of Civil Engineering, Universidad Católica San Antonio de Murcia, Campus de los Jerónimos s/n, 30107 Guadalupe, Murcia, Spain
José M. Cecilia: Department of Computer Engineering, Universitat Politècnica de València, Camí de Vera, s/n, 46022 Valencia, Spain
Julio Pérez-Sánchez: Department of Civil Engineering, Universidad Católica San Antonio de Murcia, Campus de los Jerónimos s/n, 30107 Guadalupe, Murcia, Spain

IJERPH, 2020, vol. 17, issue 4, 1-14

Abstract: 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 2 CV (cross-validated coefficient of determination) for the best-fit models.

Keywords: multilayer neural network (MLNN); support vector regression (SVR); water quality; eutrophication; chlorophyll-a; Mar Menor coastal lagoon (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/4/1189/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/4/1189/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:4:p:1189-:d:319992

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().