Use of Artificial Neural Networks as a Predictive Tool of Dissolved Oxygen Present in Surface Water Discharged in the Coastal Lagoon of the Mar Menor (Murcia, Spain)

Eva M. García del Toro, Luis Francisco Mateo, Sara García-Salgado, M. Isabel Más-López and Maria Ángeles Quijano
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Eva M. García del Toro: Departamento de Ingeniería Civil: Hidráulica y Ordenación del Territorio ETSI Civil, Universidad Politécnica de Madrid Alfonso XII, 3, 28014 Madrid, Spain
Luis Francisco Mateo: Departamento de Ingeniería Civil: Construcción, Infraestructura y Transporte ETSI Civil, Universidad Politécnica de Madrid Alfonso XII, 3, 28014 Madrid, Spain
Sara García-Salgado: Departamento de Ingeniería Civil: Hidráulica y Ordenación del Territorio ETSI Civil, Universidad Politécnica de Madrid Alfonso XII, 3, 28014 Madrid, Spain
M. Isabel Más-López: Departamento de Ingeniería Civil: Construcción, Infraestructura y Transporte ETSI Civil, Universidad Politécnica de Madrid Alfonso XII, 3, 28014 Madrid, Spain
Maria Ángeles Quijano: Departamento de Ingeniería Civil: Hidráulica y Ordenación del Territorio ETSI Civil, Universidad Politécnica de Madrid Alfonso XII, 3, 28014 Madrid, Spain

IJERPH, 2022, vol. 19, issue 8, 1-12

Abstract: The Mar Menor is a Mediterranean coastal saltwater lagoon (Murcia, Spain) that represents a unique ecosystem of vital importance for the area, from both an economic and ecological point of view. During the last decades, the intense agricultural activity has caused episodes of eutrophication due to the contribution of inorganic nutrients, especially nitrates. For this reason, it is important to control the quality of the water discharged into the Mar Menor lagoon, which can be performed through the measurement of dissolved oxygen (DO). Therefore, this article aimed to predict the DO in the water discharged into this lagoon through the El Albujón watercourse, for which two theoretical models consisting of a multiple linear regression (MLR) and a back-propagation neural network (RPROP) were developed. Data of temperature, pH, nitrates, chlorides, sulphates, electrical conductivity, phosphates and DO at the mouth of this watercourse, between January 2014 and January 2021, were used. A preliminary statistical study was performed to discard the variables with the lowest influence on DO. Finally, both theoretical models were compared by means of the coefficient of determination (R 2 ), the root mean square errors (RMSE) and the mean absolute error (MAE), concluding that the neural network made a more accurate prediction of DO.

Keywords: intensive agriculture; eutrophication; nitrates; dissolved oxygen (DO); multiple linear regression (MLR); back-propagation neural network (RPROP) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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