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Full-Text Articles in Fresh Water Studies
Evaluating Changes And Estimating Seasonal Precipitation For Colorado River Basin Using Stochastic Non-Parametric Disaggregation Technique, Ajay Kalra, Sajjad Ahmad
Evaluating Changes And Estimating Seasonal Precipitation For Colorado River Basin Using Stochastic Non-Parametric Disaggregation Technique, Ajay Kalra, Sajjad Ahmad
Civil and Environmental Engineering and Construction Faculty Research
Precipitation estimation is an important and challenging task in hydrology because of high variability and changing climate. This research involves (1) analyzing changes (trend and step) in seasonal precipitation and (2) estimating seasonal precipitation by disaggregating water year precipitation using a k-nearest neighbor (KNN) nonparametric technique for 29 climate divisions encompassing the Colorado River Basin. Water year precipitation data from 1900 to 2008 are subdivided into four seasons (i.e., autumn, winter, spring, and summer). Two statistical tests (Mann-Kendall and Spearman’s rho) are used to evaluate trend changes, and a rank sum test is used to identify the step change in …
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 …