Paper No. JAWRA-10-0136-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.
Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande†
Article first published online: 6 SEP 2011
© 2011 American Water Resources Association
JAWRA Journal of the American Water Resources Association
Volume 48, Issue 1, pages 10–23, February 2012
How to Cite
Abudu, S., King, J. P. and Sheng, Z. (2012), Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. JAWRA Journal of the American Water Resources Association, 48: 10–23. doi: 10.1111/j.1752-1688.2011.00587.x
- Issue published online: 1 FEB 2012
- Article first published online: 6 SEP 2011
- Received August 31, 2010; accepted June 10, 2011.
- performance characteristics;
- time series analysis;
- artificial neural networks;
- water quality;
- dissolved solids;
- Rio Grande
Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10-23. DOI: 10.1111/j.1752-1688.2011.00587.x
Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function-noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross-correlation statistical tests. The chi-square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one- to three-month-ahead model forecasts for the testing period of 1984-1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one-month-ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two-month-ahead and three-month-ahead forecasts. For three-month-ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one- to three-month-ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.