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Secchi Disk Depth Estimation from Water Quality Parameters: Artificial Neural Network versus Multiple Linear Regression Models?

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Abstract

In the present investigation, a new model based on feedforward neural networks (FFNN) is developed and compared to the standard multiple linear regression (MLR) in modeling Secchi disk depth (SD) in the Saginaw Bay, Lake Huron, Michigan, USA. The model uses four water quality parameters as input, namely total suspended solids (TSS), water temperature (TE), dissolved oxygen (DO) and chlorophyll (Chl). In an attempt to identify the important parameters that influence the SD, four water quality parameters were selected for further investigation. The analysis identified TSS and Chl to have the most important influence on the SD; and the inclusion of DO and TE did not lead to an overall improvement in the performance of the models. The FFNN and MLR were evaluated using well-known statistical indices, i.e., the correlation coefficient (CC), the root mean squared error (RMSE) and the mean absolute error (MAE). The results obtained from the present investigation are very promising, as we demonstrated that the Secchi disk depth can be predicted very well with correlation coefficient equal to 0.918 in the testing phase.

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Acknowledgments

We would like to thank Professors: Gregory Lang, Thomas H. Johengen, and Henry A. Vanderploeg from the Great Lakes Environmental Research Laboratory, NOAA, Michigan, USA, for giving permission for using the data that made this study possible. Once again, we would like to thank anonymous reviewers and the editor of Environmental Processes 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. Secchi Disk Depth Estimation from Water Quality Parameters: Artificial Neural Network versus Multiple Linear Regression Models?. Environ. Process. 3, 525–536 (2016). https://doi.org/10.1007/s40710-016-0144-4

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