1. U.S. Department of the Interior U.S. Geological Survey Development of Inferential Sensors for Real-time Quality Control of Water- level Data for the EDEN Network Paul Conrads SC Water Science Center GEER 2008 – Naples, FL July 30, 2008

2. Presentation Outline What is a “Inferential Sensor”? Background  Industrial application  Homeland security EDEN Network Water-level Inferential Sensor Challenges

3. Tough Environment to Monitor Emissions regulations require measurements of effluent gases Smoke stack burns up probes Need alternative to “hard” sensors

4. Hard Sensor vs. Inferential Sensor Virtual sensor replaces actual sensor  Temporary gage smoke stack  Operate industry to cover range of emissions  Develop model of emissions based on operations  Model becomes the “Inferential Sensor’

5. Industrial Application: Production Emission Monitoring System Production Control System Components. A Data Historian is a special database designed to hold massive amounts of process and laboratory time series data.

6. Production Emission Model Predicted Emissions History for a Manufactured Product.

7. Inferential Sensor Architecture PEMS Architecture

8. Industrial Benefits Optimizes Manufacturing Processes Documents Continuous Compliance Lower Cost – In some situations, a proactive intelligent system can partially or fully replace passive back end controls to reduce capital and long-term operating expenses Most Advantageous Permitting – because it actively prevents pollution Works with Existing or New Production Controls

9. If it is good enough for Industry... Use similar approach for real-time data Develop models to predict real-time data Use predictions as “soft-sensor” to:  QA/QC hard sensor  Provide accurate estimates for hard sensor  Provide redundant signal

10. Everglades

11. Water-depth and Ecosystems Differences of 1 ft can change vegetation communities

12. Everglade Depth Estimation Network (EDEN) Grid up the Everglades (400 m) Measure elevation of each grid Create digital elevation model (DEM) Create surface-water map Water depths = Surface-Water Map – DEM Goal: generate real-time water-surface and water-depth maps

13. EDEN – Model Integration

14. EDEN Gaging Network Agencies: USGS S. Florida Water Management District Big Cypress National Preserve Everglades National Park

15. EDEN Water-Surface Maps Daily medians computed from hourly data Water surface maps generated using radial basis function (RBF) in GIS

16. EDEN Water-Surface Map Bad values creates erroneous maps

17. Problem Need to minimize missing and erroneous data Approach Develop “soft” sensors for redundant signal Inferential Sensor application

18. Hypothetical Case Site B Site A Create model (Inferential Sensor) for Site B using Site A as an input Decide when to use Inferential Sensor instead of gage data Actual application would be for 253 stations

19. Hypothetical Case: Gage Data Gage Height, in feet Possible Outliers Missing Data

20. Hypothetical Case: Inferential Sensor Gage Height, in feet How to decide when to use the inferential sensor? 1. 95 th confidence interval of model 2. Constant distance from inferential sensor R 2 =0.94

21. Hypothetical Case: When to use Inferential Sensor? Gage Height, in feet

22. Hypothetical Case: When to use Inferential Sensor? Gage Height, in feet

23. Hypothetical Case - comments Issue of model accuracy Immediate benefit for missing data Made the assumption that Site A was correct Example used daily data  Use inferential sensor on hourly data  Compare daily medians (used for EDEN maps) What if data for Site A is missing? Issues are magnified when dealing with a network of 253 gages

24. Initial Approach: Data Evaluation Need to know what data is good Set of filters to evaluate data quality Robust series of thresholds  Differences with other gages  Time delays and moving window averages  Time derivatives  Rate of change over various time intervals Create subset of good data

25. Initial Approach: Model Development One Approach – Canned Models  Create multiple models for a gage  Set priority for model to use depending on available data  Large number of models  Not all combinations of gages would be addressed

26. Initial Approach: Model Development Second Approach – Model on the Fly  Subset of good data, determine input data with the highest correlation  Develop models based on available data  Issue of evaluation of models  More complex programming than first approach

27. EDEN Inferential Sensor Architecture Data from NWIS – 253 sites Data Evaluation – robust filters Inferential Sensor Models Data Fill and Replacement Output Log File Data to EDEN server

28. Summary Inferential Sensor may provide an approach for:  Real-time QA/QC  Redundant signal Challenges  Identifying good data  Highly accurate models  Managing number of models Develop prototype for EDEN Stay tuned

29. Questions?