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Hydrology 2018, 5(3), 37; https://doi.org/10.3390/hydrology5030037

A Time Series Model Comparison for Monitoring and Forecasting Water Quality Variables

1
ESTGA—Águeda School of Technology and Management, University of Aveiro, Apartado 473, 3754- 909 Águeda, Portugal
2
CIDMA—Center for Research & Development in Mathematics and Applications, University of Aveiro, 3810-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Received: 18 June 2018 / Revised: 23 July 2018 / Accepted: 24 July 2018 / Published: 26 July 2018
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Abstract

The monitoring and prediction of water quality parameters are important tasks in the management of water resources. In this work, the performances of time series statistical models were evaluated to predict and forecast the dissolved oxygen (DO) concentration in several monitoring sites located along the main river Vouga, in Portugal, during the period from January 2002 to May 2015. The models being compared are a regression model with correlated errors and a state-space model, which can be seen as a calibration model. Both models allow the incorporation of water quality variables, such as time correlation or seasonality. Results show that, for the DO variable, the calibration model outperforms the regression model for sample modeling, that is, for a short-term forecast, while the regression model with correlated errors has a better performance for the forecasting h-steps ahead framework. So, the calibration model is more useful for water monitoring using an online or real-time procedure, while the regression model with correlated errors can be applied in order to forecast over a longer period of time. View Full-Text
Keywords: monthly data; time series analysis; state-space modeling; regression modeling; autoregressive errors; dissolved oxygen concentration monthly data; time series analysis; state-space modeling; regression modeling; autoregressive errors; dissolved oxygen concentration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Monteiro, M.; Costa, M. A Time Series Model Comparison for Monitoring and Forecasting Water Quality Variables. Hydrology 2018, 5, 37.

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