Please use this identifier to cite or link to this item:
Title: A Time Series Model Comparison for Monitoring and Forecasting Water Quality Variables
Author: Monteiro, Magda
Costa, Marco
Keywords: Monthly data
Time series analysis
State-space modeling
Regression modeling
Autoregressive errors
Dissolved oxygen concentration
Issue Date: Jul-2018
Publisher: MDPI
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.
Peer review: yes
DOI: 10.3390/hydrology5030037
ISSN: 2306-5338
Publisher Version:
Appears in Collections:CIDMA - Artigos
ESTGA - Artigos
PSG - Artigos

Files in This Item:
File Description SizeFormat 
artigo.pdf668.49 kBAdobe PDFView/Open

Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.