Abstract
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 \(\overline{\sigma }\) 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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Acknowledgments
This work is part of the research projects RTI2018–098160-B-I00 supported by MICINN (Ministerio de Ciencia e Innovación-Spain) and “EnviroPorts. Investigación de técnicas de análisis y predicción de series temporales de parámetros ambientales y su interrelación con tráfico marítimo en entornos portuarios”. Expediente 2021.08.CT01.000044. Financiado por el Instituto de Fomento de la Región de Murcia (INFO) dentro del PROGRAMA DE AYUDAS DESTINADAS A CENTROS TECNOLÓGICOS DE LA REGIÓN DE MURCIA DESTINADAS A LA REALIZACIÓN DE ACTIVIDADES I+D DE CARÁCTER NO ECONÓMICO. MODALIDAD 1: “PROYECTOS I+D INDEPENDIENTE”.
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González-Enrique, J. et al. (2023). Deep Learning Approach for the Prediction of the Concentration of Chlorophyll ɑ in Seawater. A Case Study in El Mar Menor (Spain). In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_8
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