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Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA

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Abstract

This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.

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Acknowledgments

We would like to thank Paul Wintrob (US EPA, Region 1, Boston) and Bill Walsh-Rogalski (USEPA Charles River) and all EPA scientists and water quality managers from Charles Rivers buoy, for allowing permission for using the data that made this study possible. Once again, we would like to thank the anonymous reviewers and the editor of Environmental Science and Pollution Research for their invaluable comments and suggestions on the contents of the manuscript which significantly improved the quality of the paper.

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  1. Faculty of Science, Agronomy Department, Hydraulics Division University, 20 Août 1955, Route EL Hadaik, BP 26, Skikda, Algeria

    Salim Heddam

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Heddam, S. Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA. Environ Sci Pollut Res 23, 17210–17225 (2016). https://doi.org/10.1007/s11356-016-6905-9

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