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Neural Network Forecasting of Water Levels along the Texas Gulf Coast. Philippe Tissot * , Daniel Cox ** , Patrick Michaud * Zack Bowles * , Jeremy Stearns * , Alex Drikitis * * Conrad Blucher Institute, Texas A&M University-Corpus Christi

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Neural network forecasting of water levels along the texas gulf coast

Neural Network Forecasting of Water Levels along the Texas Gulf Coast

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

Zack Bowles*, Jeremy Stearns*, Alex Drikitis*

* Conrad Blucher Institute, Texas A&M University-Corpus Christi

* * Hinsdale Wave Research Laboratory, Oregon State University


Presentation outline
Presentation Outline Gulf Coast

  • Introduction: Tides & Water Level Forecasts

  • Application of ANN Modeling to Water Level Forecasts in the Corpus Christi Estuary

  • Test of the Model for Tropical Storms and Hurricanes

  • Conclusions


Tides
Tides Gulf Coast

  • Definition: Tides are caused by the gravitational pull of the Sun and Moon on the waters of the Earth

  • Difference between tides and water levels

  • How well do the tide table work along the Gulf Coast?



Tidal charts performance along the texas coast 1997 2001
Tidal Charts Performance along the Texas Coast (1997-2001) 1998)

Sab. RMSE=0.16

CF=70.09

Pleasure Pier RMSE=0.16

CF=71.65

Pier 21 RMSE=0.15

CF=74.37

BHP RMSE=0.12

CF=82.71

Coast Guard RMSE=0.12

CF=81.7

Port Isab. RMSE=0.10

CF=89.1


Water level changes and tides
Water Level Changes and Tides 1998)

  • There is a large non tidal related component for water level changes on the Texas coast

  • Other factors influencing water level changes:


Study area corpus christi estuary
Study Area: Corpus Christi Estuary 1998)

Port Aransas

Nueces Bay

Ingleside

Aquarium

Corpus Christi Bay

Oso Bay

Gulf of Mexico

Naval Air Station

Port of Corpus Christi

Packery Channel

Bob Hall Pier


TCOON Data Streams in the Corpus Christi Estuary 1998)

  • 6 TCOON Stations Measuring:

  • Water levels (6)

  • Wind speeds (4)

  • Wind directions (4)

  • 10 x 8760 hourly measurements per year

  • Barometric pressure

  • Air temperature

  • Water temperature

Port Aransas

Aquarium

Ingleside

Nueces Bay

Corpus Christi Bay

Gulf of Mexico

Naval Air Station

Packery Channel

Oso Bay

Port of Corpus Christi

Bob Hall Pier


  • Problem: 1998) The tide charts do not work for most of the Texas coast

  • Opportunity: We have extensive time series of water level and weather measurements for most of the Texas coast


Data intensive modeling
Data Intensive Modeling 1998)

  • Real time data availability is rapidly increasing

  • Cost of weather sensors and telecommunication equipment is steadily decreasing while performance is improving

  • How to use these new streams of data / can new modeling techniques be developed


Data intensive modeling1
Data Intensive Modeling 1998)

  • Classic models (large computer codes - finite elements based) need boundary conditions and forcing functions which are difficult to provide during storm events

  • Neural Network modeling can take advantage of high data density and does not require the explicit input of boundary conditions and forcing functions

  • The modeling is focused on forecasting water levels at specific locations


Neural network modeling
Neural Network Modeling 1998)

  • Started in the 60’s

  • Key innovation in the late 80’s: Backpropagation learning algorithms

  • Number of applications has grown rapidly in the 90’s especially financial applications

  • Growing number of publications presenting environmental applications


Neural network features
Neural Network Features 1998)

  • Non linear modeling capability

  • Generic modeling capability

  • Robustness to noisy data

  • Ability for dynamic learning

  • Requires availability of high density of data



Neural network forecasting of water levels
Neural Network Forecasting of Water Levels 1998)

Water Level History

 (X1+b1)

 (a1,ixi)

Wind Stress History

 (X3+b3)

b1

 (a3,ixi)

H (t+i)

Wind Stress Forecast

b3

Water Level Forecast

 (a2,ixi)

 (X2+b2)

Barometric Pressure History

b2

Input Layer

Hidden Layer

Output Layer

Philippe Tissot - 2000


Activation functions
Activation Functions 1998)

radbas

tansig

purelin

logsig


Training of a neural network
Training of a Neural Network 1998)

Philippe Tissot - 2000





Persistence model
Persistence Model Data Set)

  • The water anomaly builds progressively especially for the embayment location

  • Persistent model: assume that the water anomaly at the time of forecasts will persist throughout the forecasting period

  • Compare the ANN results with the Persistence model


Performance measurements
Performance Measurements Data Set)

Average error: Eavg = (1/N)  ei

Absolute Average Error: Eavg = (1/N) ei

Root Mean Square Error: Erms = ((1/N)  ei2)1/2

CF(X) – Central Frequency or percentage of the forecasts within +/- 15 cm of actual measurement

POF(X) – Positive Outlier Frequency or percentage of the forecasts X cm or more above the actual measurement.

NOF(X) – Negative Outlier Frequency or percentage of the forecasts X cm or more below the actual measurement.

MDPO(X) – Maximum Duration of Positive Outlier.

MDNO(X) – Maximum Duration of Negative Outlier.


Performance analysis of the model for bhp and ccnas
Performance Analysis of the Model for BHP and CCNAS Data Set)

  • Five 1-year data sets: ‘97, ‘98, ’99, ’00, ‘01 including water level and wind measurements, tidal forecasts and wind hindcasts

  • 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


Bhp performance analysis
BHP Performance Analysis Data Set)

harmonic forecasts (blue/squares), Persistence model (green/diamonds), ANN model without wind forecasts (red dashed/triangles) and ANN model with wind forecasts (red/circles)


Ccnas performance analysis
CCNAS Performance Analysis Data Set)

Harmonic forecasts (blue/squares), Persistent model (green/diamonds), ANN model with only NAS data (red dashed/triangles) and ANN model with additional BHP data (red/circles)


Comparison of ann harmonic forecasts for 24 hour forecasts 97 01

CCNAS Data Set)

Tide Tables

ANN Model

BHP

Tide Tables

ANN Model

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 ANN & Harmonic Forecasts for 24 Hour Forecasts (’97-’01)


 Packery Channel Data Set)

Tide Tables

Tide Tables

ANN Model

ANN Model

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 ANN & Harmonic Forecasts for 24 Hour Forecasts (’97-’01)

Port Aransas




Model assessment for non storm conditions
Model Assessment for non Storm Conditions Data Set)

  • ANN models and Persistence model improve considerably on the harmonic forecasts during regular conditions and frontal passages

  • ANN and Persistence models are being implemented as part of TCOON


Tropical storms and hurricanes
Tropical Storms and Hurricanes Data Set)

  • Need for short to medium term water level forecasts during tropical storms and hurricanes

  • Tropical storms and hurricanes are relatively infrequent and have each their own characteristics.

  • ANN model performance?


Tropical storm frances september 7 17 1998
Tropical Storm Frances - September 7-17, 1998 Data Set)

Frances Trajectory

Landfall on Sept. 11


Ccnas ann 12 hour forecasts during 1998 tropical storm frances ann trained over 2001 data set
CCNAS ANN 12-hour Forecasts During 1998 Tropical Storm Frances (ANN trained over 2001 Data Set)


Ccnas ann 24 hour forecasts during 1998 tropical storm frances ann trained over 2001 data set
CCNAS ANN 24-hour Forecasts During 1998 Tropical Storm Frances (ANN trained over 2001 Data Set)


Storm Name Frances (ANN trained over 2001 Data Set)

Storm Type at Landfall

Landfall Locations

Landfall Date

Lili

H

Vermillion Bay

10/03/2002

Isidore

TS

New Orleans

9/26/2002

Faye

TS

Palacios

9/7/2002

Bertha

D

Port Mansfield

8/9/2002

2002 Tropical Storms and Hurricanes


Isidore Frances (ANN trained over 2001 Data Set)

Landfall 9/26/2002, near New Orleans


Effect on Water Levels of 2002 Tropical Storms and Hurricanes

Isidore

Isidore

Faye

Faye

Lili

Lili

Bertha

Bertha

NAS: up to ~ + 80 cm

BHP: up to ~ + 80 cm

Galveston Pleasure Pier: up to ~ + 110 cm

Sabine: up to ~ + 80 cm


Comparison of Measured and Forecasted (12-Hour) Water levels during the 2002 Tropical Storms and Hurricanes at CCNAS

Black - measurement

Blue – Harmonic

Green – Persistent

Red - ANN


Conclusions
Conclusions levels during the 2002 Tropical Storms and Hurricanes at CCNAS

  • ANN models leads to significant improvements for the forecasting of water levels in general and especially during frontal passages

  • Computationally and financially inexpensive method

  • The quality of the wind forecasts will likely be the limiting factor for the accuracy of the water level forecasts

  • Implementing ANN model on a number of TCOON stations

  • The persistence model results are comparable to ANN forecasts for a number of cases and a great improvement over tide tables in all cases


Ongoing future work
Ongoing/Future Work levels during the 2002 Tropical Storms and Hurricanes at CCNAS

  • Implement the Persistence model for most TCOON stations with the necessary water level history.

  • Implement a real time transfer of NWS Eta-12 wind forecasts into TCOON and the ANN models

  • Implement the ANN model for selected stations (~ 10) important to coastal users

  • Study and document the performance of the models (Persistent/ANN) during the past TS and Hurricanes.


Questions? levels during the 2002 Tropical Storms and Hurricanes at CCNAS



Histogram
Histogram Level Changes

(amount of errors vs. location of error)


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