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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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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


Presentation outline
Presentation Outline Forecasting Water Levels for the Corpus Christi, Texas, Estuary

  • 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


Texas coastal observation network tcoon
Texas Coastal Observation Network (TCOON) Forecasting Water Levels for the Corpus Christi, Texas, Estuary

  • Started 1988

  • Over 50 stations

  • Primary Sponsors

    • General Land Office

    • Water Devel. Board

    • US Corps of Eng

    • Nat'l Ocean Service


Typical tcoon station
Typical TCOON station Forecasting Water Levels for the Corpus Christi, Texas, Estuary

  • Wind anemometer

  • Radio Antenna

  • Satellite Transmitter

  • Solar Panels

  • Data Collector

  • Water Level Sensor

  • Water Quality Sensor

  • Current Meter


TCOON Web Site Forecasting Water Levels for the Corpus Christi, Texas, Estuary


Tides and water levels
Tides and Water Levels Forecasting Water Levels for the Corpus Christi, Texas, Estuary

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


Study site cc estuary
Study Site: CC Estuary Forecasting Water Levels for the Corpus Christi, Texas, 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


Comparison of tides and water levels
Comparison of Tides and Water Levels Forecasting Water Levels for the Corpus Christi, Texas, Estuary

Corpus Christi Naval Air Station

TCOON Measurements

Tide Tables


Comparison of tides water levels and winds squared

1 Forecasting Water Levels for the Corpus Christi, Texas, Estuary

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


Challenge
Challenge Forecasting Water Levels for the Corpus Christi, Texas, Estuary

  • 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?


Ann features for water level forecasts
ANN Features for Water Level Forecasts Forecasting Water Levels for the Corpus Christi, Texas, Estuary

  • Non linear modeling capability

  • Generic modeling capability

  • Robustness to noisy data

  • Ability for dynamic learning

  • Requires availability of high density of data


Ann model
ANN Model Forecasting Water Levels for the Corpus Christi, Texas, Estuary

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


Anns characterisitics
ANNs Characterisitics Forecasting Water Levels for the Corpus Christi, Texas, Estuary

  • 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)


Ccnas ann 24 hour forecasts
CCNAS ANN 24-hour Forecasts Forecasting Water Levels for the Corpus Christi, Texas, Estuary

ANN trained over 2001 Data Set


Ccnas ann 24 hour forecasts1
CCNAS ANN 24-hour Forecasts Forecasting Water Levels for the Corpus Christi, Texas, Estuary

ANN trained over 2001 Data Set


Ccnas ann 24 hour forecasts2
CCNAS ANN 24-hour Forecasts Forecasting Water Levels for the Corpus Christi, Texas, Estuary

ANN trained over 2001 Data Set


Model assessment
Model Assessment Forecasting Water Levels for the Corpus Christi, Texas, Estuary

  • 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


Performance analysis coastal station
Performance Analysis Forecasting Water Levels for the Corpus Christi, Texas, Estuary(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


Performance analysis estuary station
Performance Analysis Forecasting Water Levels for the Corpus Christi, Texas, Estuary(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


Performance analysis estuary station1
Performance Analysis Forecasting Water Levels for the Corpus Christi, Texas, Estuary(Estuary Station)

ANN inputs include Estuary and Coastal Measurements


Comparison of tides and ann for 24 hour forecasts

CCNAS Forecasting Water Levels for the Corpus Christi, Texas, Estuary

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


 Packery Channel Forecasting Water Levels for the Corpus Christi, Texas, Estuary

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


Tropical Storm Frances - September 7-17, 1998 Forecasting Water Levels for the Corpus Christi, Texas, Estuary

Frances Trajectory

Landfall on Sept. 11


Ccnas ann 12 hour forecasts
CCNAS ANN 12-hour Forecasts Forecasting Water Levels for the Corpus Christi, Texas, Estuary

CF(Tides) = 17 %

CF(Persistent) = 94 %

CF(NN w/o Forecasts) = 95%

CF(NN with Forecasts) = 98 %

ANN trained over 1997 Data Set


Ccnas ann 24 hour forecasts3

1.2 Forecasting Water Levels for the Corpus Christi, Texas, Estuary

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


Conclusions
Conclusions Forecasting Water Levels for the Corpus Christi, Texas, Estuary

  • 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


Questions? Forecasting Water Levels for the Corpus Christi, Texas, Estuary


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