RT journal article
T1 Chlorophyll-α forecasting using LSTM, bidirectional LSTM and GRU networks in El Mar Menor (Spain)
A1 González Enrique, Francisco Javier
A1 Rodríguez García, María Inmaculada
A1 Ruiz Águilar, Juan Jesús
A1 Carrasco García, María Gema
A1 Enguix, Ivan Felis
A1 Turias Domínguez, Ignacio José
A2 Ingeniería Industrial e Ingeniería Civil
A2 Ingeniería Informática
K1 chlorophyll
K1 machine learning
K1 deep learning
K1 LSTM
K1 Mar Menor
K1 eutrophication
K1 GRU
AB The objective of this research is to develop accurate forecasting models for chlorophyll-α concentrations at various depths in El Mar Menor, Spain. Chlorophyll-α plays a crucial role in assessing eutrophication in this vulnerable ecosystem. To achieve this objective, various deep learning forecasting techniques, including long short-term memory, bidirectional long short-term memory and gated recurrent uni networks, were utilized. The models were designed to forecast the chlorophyll-α levels with a 2-week prediction horizon. To enhance the models’ accuracy, a sliding window method combined with a blocked cross-validation procedure for time series was also applied to these techniques. Two input strategies were also tested in this approach: using only chlorophyll-α time series and incorporating exogenous variables. The proposed approach significantly improved the accuracy of the predictive models, no matter the forecasting technique employed. Results were remarkable, with values reaching approximately 0.90 for the 0.5-m depth level and 0.80 for deeper levels. The proposed forecasting models and methodologies have great potential for predicting eutrophication episodes and acting as decision-making tools for environmental agencies. Accurate prediction of eutrophication episodes through these models could allow for proactive measures to be implemented, resulting in improved environmental management and the preservation of the ecosystem.
PB Oxford University Press
SN 1367-0751
YR 2024
FD 2024
LK http://hdl.handle.net/10498/39367
UL http://hdl.handle.net/10498/39367
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026