Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain)

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.

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González-Enrique J. Ruiz-Aguilar J.J.…. Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain). SOCO, 2023. https://doi.org/10.1007/978-3-031-18050-7_8

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Tipo de recurso Artículo
Fecha de creación 05-11-2024
Fecha de última modificación 20-01-2025
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Identificador de los metadatos 06ca0bca-0592-5264-b443-8a149b7651c1
Idioma de los metadatos Español
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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 González-Enrique, J., Ruiz-Aguilar, J.J.,…
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Identificador alternativo DOI: 10.1007/978-3-031-18050-7_8
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Email del autor javier.gonzalezenrique@uca.es
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