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

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.

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Jimeno-Saez P. Senent-Aparicio J. Cecilia J.M. y Perez-Sanchez J. Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). MDPI, 2020. https://doi.org/10.3390/ijerph17041189

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Tipo de recurso Artículo
Fecha de creación 05-11-2024
Fecha de última modificación 05-11-2024
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Identificador de los metadatos fdc45613-002f-5f83-8c90-0402d42957f5
Idioma de los metadatos Español
Temáticas (NTI-RISP)
Categoría del conjunto de alto valor (HVD) Observación de la Tierra y medio ambiente
Categoría temática ISO 19115 biota
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Nombre del autor Jimeno-Saez, P., Senent-Aparicio, J., Cecilia, J.M. y Perez-Sanchez, J.
Nombre del editor MDPI
Identificador alternativo DOI: 10.3390/ijerph17041189
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Email del autor jsenent@ucam.edu
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