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Using oceanic-atmospheric oscillations for long lead time streamflow forecasting
 

Summary: Using oceanic-atmospheric oscillations for long lead time
streamflow forecasting
Ajay Kalra1
and Sajjad Ahmad1
Received 21 January 2008; revised 4 December 2008; accepted 14 January 2009; published 18 March 2009.
[1] We present a data-driven model, Support Vector Machine (SVM), for long lead time
streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on
statistical learning theory that uses a hypothesis space of linear functions based on Kernel
approach and has been used to predict a quantity forward in time on the basis of training
from past data. The strength of SVM lies in minimizing the empirical classification
error and maximizing the geometric margin by solving inverse problem. The SVM model
is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado
River Basin in the western United States. Annual oceanic-atmospheric indices,
comprising Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic
Multidecadal Oscillation (AMO), and El Nino­Southern Oscillations (ENSO) for a period
of 1906­2001 are used to generate annual streamflow volumes with 3 years lead time.
The SVM model is trained with 86 years of data (1906­1991) and tested with 10 years of
data (1992­2001). On the basis of correlation coefficient, root means square error, and
Nash Sutcliffe Efficiency Coefficient the model shows satisfactory results, and the
predictions are in good agreement with measured streamflow volumes. Sensitivity

  

Source: Ahmad, Sajjad - Department of Civil and Environmental Engineering, University of Nevada at Las Vegas

 

Collections: Engineering