Using oceanic-atmospheric oscillations for long lead time streamflow forecasting

• Main Authors: Kalra, Ajay, Ahmad, Sajjad
• Format: Article in Journal/Newspaper
• Language: English
• Published: Digital Scholarship@UNLV 2009
• Subjects: Atlantic Multidecadal Oscillation (AMO); Artificial neural network (ANN); El Nino–Southern Oscillations (ENSO); Long-range weather forecasting; Neural networks (Computer science); North America – Colorado River Watershed; North Atlantic Oscillation (NAO); Ocean-atmosphere interaction; Ocean-atmospheric oscillations; PDO; Streamflow – Forecasting; Support Vector Machines (SVM); Environmental Engineering; Environmental Sciences; Fresh Water Studies; Meteorology; Water Resource Management; Nash; Pacific; Sutcliffe; North Atlantic; North Atlantic oscillation
• Online Access: https://digitalscholarship.unlv.edu/fac_articles/100; https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=1099&context=fac_articles

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 qua...