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University of Nevada, Las Vegas
Civil and Environmental Engineering and Construction Faculty Research
- Keyword
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- Long-range weather forecasting (2)
- Artificial neural network (ANN) (1)
- Atlantic Multidecadal Oscillation (AMO) (1)
- El Nino–Southern Oscillations (ENSO) (1)
- El Niño Current (1)
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- Neural networks (Computer science) (1)
- North America – Colorado River Watershed (1)
- North Atlantic Oscillation (NAO) (1)
- Ocean temperature (1)
- Ocean-atmosphere interaction (1)
- Ocean-atmospheric oscillations (1)
- PDO (1)
- Rayleigh waves (1)
- Seismology--Analysis (1)
- Streamflow – Forecasting (1)
- Support Vector Machines (SVM) (1)
- Surface waves--Measurement (1)
- Water-supply – Forecasting (1)
Articles 1 - 3 of 3
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The 2009-2010 El Nino: Hydrologic Relief To U.S. Regions, Glenn A. Tootle, Thomas C. Piechota, Oubeidillah Aziz, William Paul Miller, Venkat Lakshmi, John A. Dracup, Carly Jerla
The 2009-2010 El Nino: Hydrologic Relief To U.S. Regions, Glenn A. Tootle, Thomas C. Piechota, Oubeidillah Aziz, William Paul Miller, Venkat Lakshmi, John A. Dracup, Carly Jerla
Civil and Environmental Engineering and Construction Faculty Research
Current forecasts by the U.S. National Oceanic and Atmospheric Administration (NOAA) are that the Pacific Ocean will experience El Niño conditions in late 2009 and into 2010. These forecasts are similar to past El Niño events in 1972–1973, 1982–1983, 1986–1987, and 2002–2003.
Evaluating the hydrologic conditions for these past El Niño events reveals that during these times, surface water supply conditions improved in many parts of the United States, including the Southeast, Midwest, and Southwest. At the same time, the Pacific Northwest and other specific regions of the United States experienced below-average water supply conditions. This is consistent with the …
Using Oceanic-Atmospheric Oscillations For Long Lead Time Streamflow Forecasting, Ajay Kalra, Sajjad Ahmad
Using Oceanic-Atmospheric Oscillations For Long Lead Time Streamflow Forecasting, Ajay Kalra, Sajjad Ahmad
Civil and Environmental Engineering and Construction Faculty Research
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 …
Role Of Forward Model In Surface-Wave Studies To Delineate A Buried High-Velocity Layer, Xiaohui Jin, Barbara Luke, Carlos Calderon-Macias
Role Of Forward Model In Surface-Wave Studies To Delineate A Buried High-Velocity Layer, Xiaohui Jin, Barbara Luke, Carlos Calderon-Macias
Civil and Environmental Engineering and Construction Faculty Research
Procedures are tested and compared for processing Rayleigh surface wave data to obtain one-dimensional shear wave velocity profiles for a hypothetical site that contains a buried high-velocity layer (HVL). The main purpose of such an investigation would be to discriminate and characterize the HVL. When target dispersion curves are derived from synthetic time histories, for the most part, the HVL is better identified when profiles are inverted using only the fundamental mode of Rayleigh wave propagation, rather than a more compatible but more complex forward model. The outcomes imply that in practice, a simple forward model might be more successful …