Emulation of DWRDSM using Artificial Neural Networks and Estimation of Sacramento River Flow from Salinity

by Nicky Sandhu,
Ralph Finch, (M.ASCE),



Document Type: Proceeding Paper

Part of: North American Water and Environment Congress & Destructive Water

Abstract: Artificial Neural Networks (ANNs) are widely used and well-suited for multiple non-linear regression. Department of Water Resources Delta Simulation Model (DWRDSM) is a computer model that simulates hydrology and constituent transport in the Sacramento-San Joaquin Delta. Feed-forward ANNs are used to relate the flow conditions and gate positions in the Delta to the DWRDSM simulated salinity at interior and boundary locations in the Delta. The ANNs provide a fast and reasonably accurate method of modeling the relationship between flows and water quality. This relationship is then used to estimate the Sacramento River flow required to meet a salinity standard.

Subject Headings: Salinity | Streamflow | Neural networks | Computer models | River flow | Hydrologic models | Model accuracy | Water resources | Water quality | Simulation models | Flow simulation | North America | California | United States | Sacramento

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