Application of Partial Least-Squares Regression in Seasonal Streamflow Forecasting
Publication: Journal of Hydrologic Engineering
Volume 15, Issue 8
Abstract
The application of partial least-squares regression (PLSR) in seasonal streamflow forecasting was investigated using snow water equivalent, precipitation, temperature from automatic Snow Telemetry sites, and previous flow conditions as input variables. The forecast performance of PLSR models was compared to principal components regression (PCR) models as well as to the Natural Resources Conservation Service (NRCS) official forecasts in three Rio Grande watersheds including the Rio Grande Headwater Basin, Conejos River Basin in Colorado, and Rio Grande Basin above Elephant Butte Reservoir, New Mexico. The results indicated that using a correlation-weighted precipitation index is a relatively effective method in both improving forecast accuracy and developing relatively parsimonious regression models. In comparison of PLSR and PCR, similar forecast accuracies were obtained for both methods in jackknife cross validation and the test period (2003–2007) although PLSR has higher calibration coefficient of determination and can reach its minimal prediction error with a smaller number of components than PCR. The comparison with NRCS official forecasts showed that the application of PLSR in seasonal streamflow forecasting is promising. This approach could be combined into NRCS’s operational forecasting environment for possible forecast improvement.
Get full access to this article
View all available purchase options and get full access to this article.
References
Abdi, H. (2003). “Partial least squares regression.” The Sage encyclopedia of social sciences research methods, M. Lewis-Beck, A. Bryman, and T. Futing, eds., Sage, Thousand Oaks, Calif., 1–7.
de Jong, S. (1993). “SIMPLS: An alternative approach to partial least squares regression.” Chemom. Intell. Lab. Syst., 18, 251–263.
Eldaw, A. K., Salas, J. D., and Garcia, L. A. (2003). “Long-range forecasting of the Nile River flows using climatic forcing.” J. Appl. Meteorol., 42(7), 890–904.
Garen, D. C. (1992). “Improved techniques in regression-based streamflow volume forecasting.” J. Water Resour. Plann. Manage., 118(6), 654–670.
Geladi, P., and Kowalski, B. (1986). “Partial least squares regression: A tutorial.” Anal. Chim. Acta, 185, 1–17.
Hsieh, W. W., Yuval, Li, J., Shabbar, A., and Smith, S. (2003). “Seasonal prediction with error estimation of Columbia River streamflow in British Columbia.” J. Water Resour. Plann. Manage., 129(2), 146–149.
Lall, U. B., and Sharma, A. (1996). “A nearest neighbor bootstrap for resampling hydrologic time series.” Water Resour. Res., 32(3), 679–693.
McCuen, R. H. (1985). Statistical methods for engineers, Prentice-Hall, Englewood Cliffs, N.J.
McCuen, R. H., Rawls, W. J., and Whaley, B. L. (1979). “Comparative evaluation of statistical methods for water supply forecasting.” Water Resour. Bull., 15(4), 935–947.
McCuen, R. H., and Snyder, W. M. (1986). Hydrologic modeling: Statistical methods and applications, Prentice-Hall, Englewood Cliffs, N.J.
Natural Resources Conservation Service (NRCS). (1997). “Southern oscillation index statistical correlation with spring runoff in the western US.” ⟨http://www.wrcc.dri.edu/enso/soiwsf2.pdf⟩ (June 16, 2008).
Natural Resources Conservation Service (NRCS). (2007). “Statistical techniques used in the VIPER water supply forecasting software.” NRCS technical note 210-2, ⟨http://directives.sc.egov.usda.gov/tn_sswsf_2_a.pdf⟩ (April 2, 2008).
Pagano, T. C., Garen, D. C., Perkins, T., and Pasteris, P. A. (2009). “Operational daily-updated seasonal water supply forecasts for the western US.” J. Am. Water Resour. Assoc., 45(3), 767–778.
Pagano, T. C., Garen, D. C., and Sorooshian, S. (2004). “Evaluation of official western U.S. seasonal water supply outlooks, 1922–2002.” J. Hydrometeor., 5(5), 896–909.
Piechota, T. C., Chiew, F. H. S., Dracup, J. A., and McMahon, T. A. (1998). “Seasonal streamflow forecasting in eastern Australia and the El Niño-southern oscillation.” Water Resour. Res., 34(11), 3035–3044.
Piechota, T. C., and Dracup, J. A. (1999). “Long-range streamflow forecasting using El-Niño southern oscillation indicators.” J. Hydrol. Eng., 4(2), 144–151.
Prairie, J. R., Rajagopalan, B., Fulp, T. J., and Zagona, E. A. (2006). “Modified K-NN model for stochastic streamflow simulation.” J. Hydrol. Eng., 11(4), 371–378.
Redmond, K. T., and Koch, R. W. (1991). “Surface climate and streamflow variability in the western United States and their relationship to large-scale circulation indices.” Water Resour. Res., 27(9), 2381–2399.
Risley, J. C., Gannett, M. W., Lea, J. K., and Roehl, E. A. (2005). “An analysis statistical methods for seasonal flow forecasting in the Upper Klamath River Basin of Oregon and California.” Scientific Investigations Rep. No. 2005-5177, USGS, ⟨http://pubs.usgs.gov/sir/2005/5177/⟩ (March 21, 2008).
SAS Institute. (2008). “SAS/STAT 9.2 user’s guide. The PLS procedure.” SAS Institute Inc., ⟨http://www.technion.ac.il/docs/sas/stat/chap51/index.htm⟩ (May 21, 2008).
Shamseldin, A. Y., and O’Connor, K. M. (1996). “A nearest neighbor linear perturbation model for river flow forecasting.” J. Hydrol., 179, 353–375.
Sharma, A., Tarboton, D. G., and Lall, U. (1997). “Streamflow simulation: A nonparametric approach.” Water Resour. Res., 33(2), 291–308.
Souza Filho, F. A., and Lall, U. (2003). “Seasonal to interannual ensemble streamflow forecasts for Ceara, Brazil: Applications of a multivariate, semi-parametric algorithm.” Water Resour. Res., 39, 1307–1320.
StatSoft Inc. (2008). “Partial least squares (PLS) (electronic textbook).” ⟨http://statsoft.eu/uk/textbook/stpls.html⟩ (May 2, 2009).
Sun, L., Ji, S., Yu, S., and Ye, J. (2009). “On the equivalence between canonical correlation analysis and orthonormalized partial least squares.” ⟨http://www.public.asu.edu/~lsun27/Publications/IJCAI_2009.pdf⟩ (May 2, 2009).
Tobias, R. (1995). “An introduction to partial least squares regression.” Proc., 12th Annual SAS Users Group Int. Conf., SAS Institute Inc., Cary, N.C., 1250–1257.
Tootle, G. A., and Piechota, T. C. (2004). “Suwannee River long range streamflow forecasts based on seasonal climate predictors.” J. Am. Water Resour. Assoc., 40(2), 523–532.
Tootle, G. A., Singh, A. K., Piechota, T. C., and Farnham, I. (2007). “Long lead-time forecasting of U.S. streamflow using partial least squares regression.” J. Hydrol. Eng., 12(5), 442–451.
van der Voet, H. (1994). “Comparing the predictive accuracy of models using a simple randomization test.” Chemom. Intell. Lab. Syst., 25, 313–323.
Wold, H. (1966). “Estimation of principal components and related models by iterative least squares.” Multivariate analysis, P. R. Krishnaiah, ed., Academic, New York, 391–420.
Wold, S. (1994). “PLS for multivariate linear modeling.” QSAR: Chemometric methods in molecular design, methods and principles in medicinal chemistry, H. van de Waterbeemd, ed., Verlag-Chemie, Weinheim, Germany, 195–218.
Wood, A. W., and Lettenmaier, D. P. (2006). “A test bed for new seasonal hydrologic forecasting approaches in the western United States.” Bull. Am. Meteorol. Soc., 87(12), 1699–1712.
Yeniay, Ö., and Göktaş, A. (2002). “A comparison of partial least squares regression with other prediction methods.” Hacettepe Journal of Mathematics and Statistics, 31, 99–111.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 15 • Issue 8 • August 2010
Pages: 612 - 623
Copyright
© 2010 ASCE.
History
Received: Nov 27, 2008
Accepted: Dec 3, 2009
Published online: Dec 5, 2009
Published in print: Aug 2010
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.