Coupling Physical and Machine Learning Models with High Resolution Information Transfer and  Rapid Update Frameworks for Environmental Applications

Coupling Physical and Machine Learning Models with High Resolution Information Transfer and  Rapid Update Frameworks for Environmental Applications

Few current modeling tools are designed to predict short-term, high-risk runoff from critical source areas (CSAs) in watersheds which are significant sources of non point source (NPS) pollution. This study couples the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model with the Clim...

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Bibliographic Details
Main Author: Sommerlot, Andrew Richard
Other Authors: Biological Systems Engineering
Format: Others
Published: Virginia Tech 2019
Subjects:
Bayesian model
critical source area
deep learning
decision support system
short term forecast
soil moisture
variable source area
Online Access:http://hdl.handle.net/10919/89893

Internet

http://hdl.handle.net/10919/89893

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