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Modeling Delta Flow-Turbidity Relationships with Artificial Neural Networks

Explore the application of Artificial Neural Networks to model Delta flow-turbidity relationships. Findings indicate ANNs faithfully emulate turbidity fate in the Delta. Steps include evaluating auto-regressive networks and CalSim implementation.

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Modeling Delta Flow-Turbidity Relationships with Artificial Neural Networks

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  1. Modeling Delta Flow-Turbidity Relationshipswith Artificial Neural Networks • CWEMF Annual Meeting • April 16, 2012 • Paul Hutton, Ph.D., P.E.

  2. Acknowledgements Dr. Sujoy Roy, Tetra Tech Dr. Limin Chen, Tetra Tech Sanjaya Seneviratne, DWR

  3. Summary Findings • Additional review is needed before firm conclusions can be reached. • ANNs appear to faithfully emulate DSM2 turbidity fate and transport during the season of interest (i.e. Dec-Feb). • ANNs appear to provide a promising foundation for representing turbidity-based regulations in CalSim.

  4. Modeling Delta Flow-Turbidity Relationshipswith Artificial Neural Networks Background Model Development Results Next Steps

  5. RPA Component 1:Protection of Adult Delta Smelt Life Stage … delta smelt have historically been entrained when first flush conditions occur in late December. In order to prevent or minimize such entrainment, Action 1 shall be initiated on or after December 20 if the 3 day average turbidity at Prisoner’s Point, Holland Cut, and Victoria Canal exceeds 12 NTU… Action 1 shall require the Projects to maintain OMR flows no more negative than -2000 cfs… Source: Remanded USFWS 2008 Biological Opinion

  6. DSM2 Turbidity Fate & Transport

  7. Modeling Delta Flow-Turbidity Relationshipswith Artificial Neural Networks Background Model Development Results Next Steps

  8. ANN Training DataDSM2 Simulations Notes: (1) DCC gates closed; (2) South Delta barriers not installed; (3) Constant Martinez & agricultural return turbidity boundary conditions

  9. ANN Model StructureMatlab Feed Forward y(t) = f(x(t-1), …., x(t-d)) Inputs = 6 boundaries (3 flow & 3 turbidity) Hidden Neurons = 10 Time delay = 1-2 days Outputs: turbidity at 6 locations

  10. ANN Model StructureBoundary Input (Daily Averages) • Flow • North Delta (Freeport + Yolo) • East Side Streams • Calculated Old & Middle Rivers • Turbidity (Flow-weighted) • North Delta (Freeport + Yolo) • East Side Streams • Vernalis

  11. 1 Sacramento River at Rio Vista San Joaquin River at Prisoners’ Point Old River at Quimby Island Middle River at Holt Old River at Bacon Island Clifton Court Forebay Entrance 1 2 3 2 4 ANN Model StructureOutput Locations 3 5 4 6 5 6

  12. ANN Model StructureTraining Process • DSM2 data points are randomly assigned: • Training 60% • Validation 20% • Testing 20% • Training data are used to compute network parameters. Intermediate results are iteratively compared with validation data until residual error is minimized. • Testing data are independent of training and validation data and are used to evaluate network predictive power.

  13. Modeling Delta Flow-Turbidity Relationshipswith Artificial Neural Networks Background Model Development Results Next Steps

  14. Model ResultsOld River @ Quimby Island (Dec-Feb)

  15. Model Results: Summary StatisticsANN Turbidity (ntu) = Ф1 + Ф2 * DSM2 Turbidity (ntu)

  16. Steady State Flow-Turbidity Relationshipas a Function of North Delta TurbidityOld River @ Quimby Island Steady State Assumptions North Delta Flow = 30,000 cfs East Side Flow = 1500 cfs Vernalis Turbidity = 30 ntu East Side Turbidity = 30 ntu

  17. Modeling Delta Flow-Turbidity Relationshipswith Artificial Neural Networks Background Model Development Results Next Steps

  18. Next Steps • Evaluate auto-regressive networks • Explore tidal input variable • Implement methodology in CalSim

  19. Next Steps (cont’d)CalSim Implementation • Decision statement: Reduce pumping as needed to increase OMR flows, thereby controlling turbidity levels as defined by existing or alternative regulations. • Develop 82-year turbidity time series for Delta inflows • Integrate information into monthly time step • Refine ANN training (and associated data) as needed

  20. Paul Hutton, Ph.D., P.E. phutton@mwdh2o.com

  21. EXTRA SLIDES

  22. Turbidity Boundary ConditionsFreeport

  23. Turbidity Boundary Conditions (cont’d)Vernalis

  24. Turbidity Boundary Conditions (cont’d) Calaveras Yolo Bypass

  25. Turbidity Boundary Conditions (cont’d) Cosumnes River Mokelumne River

  26. Model ResultsSacramento River @ Rio Vista (Dec-Feb)

  27. Model ResultsOld River @ Prisoner’s Point (Dec-Feb)

  28. Model ResultsMiddle River @ Holt (Dec-Feb)

  29. Model ResultsOld River @ Bacon Island (Dec-Feb)

  30. Model ResultsClifton Court Forebay Entrance (Dec-Feb)

  31. Model Results2009-10 Historical Conditions Old River @ Quimby Island Sacramento River @ Rio Vista

  32. 2009-10 Historical Conditions

  33. Steady State Flow-Turbidity Relationshipas a Function of North Delta TurbiditySacramento River @ Rio Vista Steady State Assumptions North Delta Flow = 30,000 cfs East Side Flow = 1500 cfs Vernalis Turbidity = 30 ntu East Side Turbidity = 30 ntu

  34. Steady State Flow-Turbidity Relationshipas a Function of North Delta TurbiditySan Joaquin River @ Prisoner’s Point Steady State Assumptions North Delta Flow = 30,000 cfs East Side Flow = 1500 cfs Vernalis Turbidity = 30 ntu East Side Turbidity = 30 ntu

  35. Steady State Flow-Turbidity Relationshipas a Function of North Delta TurbidityMiddle River @ Holt Steady State Assumptions North Delta Flow = 30,000 cfs East Side Flow = 1500 cfs Vernalis Turbidity = 30 ntu East Side Turbidity = 30 ntu

  36. Steady State Flow-Turbidity Relationshipas a Function of North Delta TurbidityOld River @ Bacon Island Steady State Assumptions North Delta Flow = 30,000 cfs East Side Flow = 1500 cfs Vernalis Turbidity = 30 ntu East Side Turbidity = 30 ntu

  37. Steady State Flow-Turbidity Relationshipas a Function of North Delta TurbidityClifton Court Forebay Entrance Steady State Assumptions North Delta Flow = 30,000 cfs East Side Flow = 1500 cfs Vernalis Turbidity = 30 ntu East Side Turbidity = 30 ntu

  38. Steady State Flow-Turbidity Relationshipas a Function of North Delta TurbidityClifton Court Forebay Entrance Steady State Assumptions North Delta Flow = 30,000 cfs East Side Flow = 1500 cfs Vernalis Turbidity = 100 ntu East Side Turbidity = 30 ntu

  39. ANN Model StructureMatlab Autoregressive y(t) = f(x(t-1), …., x(t-d)) Boundary Inputs = 6 (3 flow & 3 turbidity) Recursive Input = 6 (turbidity) Hidden Neurons = 10 Time delay = 1-4 days Outputs: turbidity at 6 locations

  40. Spring-Neap Effect on TurbidityClifton Court Forebay Entrance 1994-95 Study 4 Study 10

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