A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes
Abstract
Recently, a stochastic data-driven framework was introduced for forecasting uncertain multiscale hydrological and water resources processes (e.g., streamflow, urban water demand (UWD)) that uses wavelet decomposition of input data to address multiscale change and stochastics to account for input variable selection, parameter, and model output uncertainty (Quilty et al., 2019). The former study considered all sources of uncertainty together. In...
Paper Details
Title
A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes
Published Date
Aug 1, 2020
Volume
130
Pages
104718 - 104718
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