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Philippe Tissot*, Patrick Michaud*, Daniel Cox**

Optimization and Performance of a Neural Network Model Forecasting Water Levels for the Corpus Christi, Texas, Estuary. Philippe Tissot*, Patrick Michaud*, Daniel Cox** *Texas A&M University-Corpus Christi, Corpus Christi, Texas **Oregon State University, Corvallis, Oregon.

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Philippe Tissot*, Patrick Michaud*, Daniel Cox**

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  1. Optimization and Performance of a Neural Network Model Forecasting Water Levels for the Corpus Christi, Texas, Estuary Philippe Tissot*, Patrick Michaud*, Daniel Cox** *Texas A&M University-Corpus Christi, Corpus Christi, Texas **Oregon State University, Corvallis, Oregon

  2. Presentation Outline • Texas Coastal Ocean Observation Network (TCOON) • Tides and Water Levels in the Gulf of Mexico • Artificial Neural Network Forecasting of Water Levels and Application to the Corpus Christi Estuary • ANN Performance for Water Level Forecasting • ANN performance during a Tropical Storm • Conclusions

  3. Texas Coastal Observation Network (TCOON) • Started 1988 • Over 50 stations • Primary Sponsors • General Land Office • Water Devel. Board • US Corps of Eng • Nat'l Ocean Service

  4. Typical TCOON station • Wind anemometer • Radio Antenna • Satellite Transmitter • Solar Panels • Data Collector • Water Level Sensor • Water Quality Sensor • Current Meter

  5. TCOON Web Site

  6. Tides and Water Levels Tide: The periodic rise and fall of a body of water resulting from gravitational interactions between Sun, Moon, and Earth. Tide and Current Glossary, National Ocean Service, 2000 Water Levels: Astronomical + Meteorological forcing + Other effects

  7. Study Site: CC Estuary Nueces Bay Port Aransas Ingleside Aquarium Corpus Christi Bay Gulf of Mexico Port of Corpus Christi Oso Bay Naval Air Station PackeryChannel Bob Hall Pier

  8. Comparison of Tides and Water Levels Corpus Christi Naval Air Station TCOON Measurements Tide Tables

  9. 1 0.5 0 -0.5 0 50 100 150 200 250 300 350 400 Comparison of Tides, Water Levels, and Winds (squared) Water Level (m) 0.5 Water Anomaly (m) 0 -0.5 0 50 100 150 200 250 300 350 400 500 N-S Wind Squared 0 -500 0 50 100 150 200 250 300 350 400 200 0 E-W Wid Squared -200 -400 0 50 100 150 200 250 300 350 400 Julian Day,1997

  10. Challenge • Develop a water level forecasting model that captures the non linear relationship between wind forcing and future water level changes • Take advantage of the large amount of real-time data available through TCOON • Artificial Neural Network Model?

  11. ANN Features for Water Level Forecasts • Non linear modeling capability • Generic modeling capability • Robustness to noisy data • Ability for dynamic learning • Requires availability of high density of data

  12. ANN Model Observed Water Levels  (X1+b1)  (a1,ixi) Observed Winds  (X3+b3) b1  (a3,ixi) H (t+i) Forecasted Winds b3 Water Level Forecast  (a2,ixi)  (X2+b2) Observed Barometric Pressures b2 Input Layer Output Layer Hidden Layer

  13. ANNs Characterisitics • ANN models developed within the Matlab (R13) and Matlab NN Toolbox environment • Simple ANNs are optimum • Use of ‘tansig’ and ‘purelin’ functions • Levenberg-Marquardt training algorithm • ANN Trained over 1 year of hourly data (8750 forecasts)

  14. CCNAS ANN 24-hour Forecasts ANN trained over 2001 Data Set

  15. CCNAS ANN 24-hour Forecasts ANN trained over 2001 Data Set

  16. CCNAS ANN 24-hour Forecasts ANN trained over 2001 Data Set

  17. Model Assessment • Based on five 1-year data sets: ‘97, ‘98, ’99, ’00, ‘01 including observed water levels and winds, and tide forecasts • Train the NN model using one data set e.g. ‘97 for each forecast target, e.g. 12 hours • Apply the NN model to the other four data sets, • Repeat the performance analysis for each training year and forecast target and compute the model performance and variability

  18. Performance Analysis(Coastal Station) 0.10 0.08 0.06 Average Absolute Forecasting Error [m] 0.04 Tides Persistent Model ANN model w/o Wind Forecasts ANN model with Wind Forecasts 0.02 0.00 0 hr 6 hr 12 hr 18 hr 24 hr 30 hr 36 hr 42 hr 48 hr 54 hr Forecasting Period

  19. Performance Analysis(Estuary Station) 0.10 0.08 0.06 Average Absolute Forecasting Error [m] 0.04 Tides Persistent Model ANN model ANN model (plus Coastal Obs.) 0.02 0.00 0 hr 6 hr 12 hr 18 hr 24 hr 30 hr 36 hr 42 hr 48 hr 54 hr Forecasting Period

  20. Performance Analysis(Estuary Station) ANN inputs include Estuary and Coastal Measurements

  21. CCNAS Tides ANN BHP (Coastal) Tides ANN Average error (bias) -2.6  2.4 -0.1  1.1 cm Average error (bias) Average Absolute error -2.7  2.9 cm 8.5  1.5 cm 4.5  0.4 cm -0.4  1.7 cm Average Absolute error Normalized RMS error 8.9  1.5 cm 0.40  0.05 0.21  0.01 6.0  0.6 cm POF (15 cm) Normalized RMS error 0.29  0.05 4.8%  1.1% 0.9%0.4% 0.20  0.02 NOF (15 cm POF (15 cm) 4.5%  1.9% 11.4%5.6% 1.3%1.4% 2.6%  1.3% NOF (15 cm) MDPO (15 cm) 12.8%6.8% 103  31 hrs 19  6 hrs 3.8%2.6% MDPO (15 cm) MDNO (15 cm) 205177 hrs 67  25 hrs 29  33 hrs 24  7 hrs MDNO (15 cm) 103  67 hrs 39  34 hrs Comparison of Tides and ANN for24- Hour Forecasts

  22.  Packery Channel Tides Tides ANN ANN Average error (bias) Average error (bias) -2.4  2.6 cm -2.6  2.2 cm -0.2  0.8 cm -0.2  1.3 cm Average Absolute error Average Absolute error 8.4  1.4 cm 7.6  1.6 cm 3.5  0.4 cm 5.2  0.5 cm Normalized RMS error Normalized RMS error 0.31  0.05 0.45  0.07 0.21  0.03 0.19  0.02 POF (15 cm) POF (15 cm) 4.6%1.8% 2.6%1.1% 0.4%  0.3% 1.8%  0.6% NOF (15 cm) NOF (15 cm) 11.1%5.9% 9.6%6.4% 1.0%  1.3% 2.2%  2.2% MDPO (15 cm) MDPO (15 cm) 74  21 hrs 77  41 hrs 14  10 hrs 23  7 hrs MDNO (15 cm) MDNO (15 cm) 123  81 hrs 201187 hrs 30  38 hrs 31  37 hrs Comparison of Tides and ANN for 24- Hour Forecasts Port Aransas

  23. Tropical Storm Frances - September 7-17, 1998 Frances Trajectory Landfall on Sept. 11

  24. CCNAS ANN 12-hour Forecasts CF(Tides) = 17 % CF(Persistent) = 94 % CF(NN w/o Forecasts) = 95% CF(NN with Forecasts) = 98 % ANN trained over 1997 Data Set

  25. 1.2 1 0.8 0.6 Water Levels (m) 0.4 0.2 0 230 240 250 260 270 280 Julian Day,1998 CCNAS ANN 24-hour Forecasts CF(Tides) = 17 % CF(Persistent) = 92 % CF(NN w/o Forecasts) = 82% CF(NN with Forecasts) = 85 % ANN trained over 1997 Data Set

  26. Conclusions • ANN models improve considerably on the tides for regular conditions and frontal passages • Once trained computationally very efficient • Allow great modeling flexibility • Accuracy and location of the Wind forecasts will determine model performance beyond 15 hours • Promising for short term, up to 12 hours, water level forecasts during storms

  27. Questions?

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