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Machine Learning Modeling Techniques for Forecasting the Trophic Level in a Restored South Mediterranean Lagoon Using Chlorophyll-a

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Abstract

An Artificial Neural Network (ANN), a Machine Learning (ML) modeling approach is proposed to predict the ecological state of the North Lagoon of Tunis, a shallow restored Mediterranean coastal ecosystem. A Nonlinear Auto Regressive with exogenous input (NARX) neural network model was fitted to predict Chlorophyll-a (Chl-a) concentrations in the North Lagoon of Tunis as an eutrophication indicator. The modeling is based on approximately three decades of monitoring water quality data (from January 1989 to April 2018) to train, validate and test the NARX model. The most relevant predictor variables used in this model were those having a high permutation importance ranking with Random Forest (RF) model, which simplified the structure of the network, resulting in a more accurate and efficient procedure. Those predictor variables are secchi depth, and dissolved oxygen. Various model scenarios with different NARX architectures were tested for Chl-a prediction. To verify the model performances, the trained models were applied to field monitoring data. Results indicated that the developed NARX model can predict Chl-a concentrations in the North Lagoon of Tunis with high accuracy (R = 0.79; MSE = 0.31). In addition, results showed that the NARX model generally performed better than multivariate linear regression (MVLR). This approach could provide a quick assessment of Chl-a variations for lagoon management and eco-restoration.

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Data Availability

The data provided by the Al-Buhaira Invest company to the first author are acknowledged.

Code Availability

Neural Net Time Series Toolbox and Regression Learner Toolbox applications were used in the MATLAB® software (version 9.3.0.948333 (R2017b), The Mathworks, MA, USA).

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Acknowledgements

We thank Al-Buhaira Invest company for providing the first author with the data.

Funding

No funding has been provided. However, the field work and laboratory analyses were a part of an institutional activity (Al-Buhaira Invest company).

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Authors and Affiliations

  1. Département Génie Halieutique et Environnement, Unité de Recherche Ecosystèmes et Ressources Aquatiques, Université de Carthage, Institut National Agronomique de Tunisie, UR03AGRO1, 43 Avenue Charles Nicolle, 1082, Tunis, Tunisia

    Nadia Ben Hadid & Abdessalem Shili

  2. ESPACE-DEV, Univ Montpellier, IRD, Univ Antilles, Univ Guyane, Univ Réunion, Montpellier, France

    Nadia Ben Hadid, Catherine Goyet & Veronique Guglielmi

  3. Laboratoire IMAGES-ESPACE-DEV, Univ. Perpignan Via Domitia, Perpignan, France

    Nadia Ben Hadid, Catherine Goyet & Veronique Guglielmi

  4. Département Génie Rural, Eaux et Forêts (GREF), Université de Carthage, Institut National Agronomique de Tunisie, Laboratoire de Grande Culture (LGC-INRAT), 43 Avenue Charles Nicolle, 1082, Tunis, Tunisia

    Hatem Chaar

  5. Société Al Buhaira de Développement et d’investissement, Les Berges du Lac, Rue de Rodrigo de Freitas, 1053, Tunis, Tunisia

    Naceur Ben Maiz

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  1. Nadia Ben Hadid
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  2. Catherine Goyet
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Contributions

NBH wrote the paper, collected data, conceived, designed and performed the analysis. CG helped in the correction of the paper and contributed in the designing of the analysis. HC helped in conceiving and the designing of the analysis and was a major contributor in writing the manuscript. NBM provided a big sequence of the monthly field investigation data. VG contributed in implementing and working with the software. AS helped in the supervision of this work and was a major contributor in writing the manuscript. All authors read and approved the final manuscript.

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Correspondence to Nadia Ben Hadid.

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Hadid, N.B., Goyet, C., Chaar, H. et al. Machine Learning Modeling Techniques for Forecasting the Trophic Level in a Restored South Mediterranean Lagoon Using Chlorophyll-a. Wetlands 41, 111 (2021). https://doi.org/10.1007/s13157-021-01479-6

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