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

  1. González-Enrique, Javier
  2. Ruiz-Aguilar, Juan Jesús
  3. Madrid Navarro, Eduardo
  4. Martínez Álvarez-Castellanos, Rosa
  5. Felis Enguix, Ivan
  6. Jerez, José M.
  7. Turias, Ignacio J.
  1. 1 Intelligent Modelling of Systems Research Group, ETSI Algeciras, University of Cádiz, Avd. Ramón Puyol s/n, 11202, Cádiz, Algeciras, Spain
  2. 2 Centro Tecnológico Naval y del Mar (CTN), 30320 Fuente Álamo, Murcia, Spain
  3. 3 Department of Computer Science, ETS Computer Science, University of Málaga, Bulevar Louis Pasteur, 35. Campus de Teatinos, 29071, Málaga, Spain
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Actas:
17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022)

ISSN: 2367-3370,  2367-3389

ISBN: 9783031180491,  9783031180507

Año de publicación: 2022

Páginas: 72-85

Tipo: Aportación congreso

DOI: 10.1007/978-3-031-18050-7_8 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

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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