neural network forecasting of water levels along the texas gulf coast n.
Download
Skip this Video
Download Presentation
Neural Network Forecasting of Water Levels along the Texas Gulf Coast

Loading in 2 Seconds...

play fullscreen
1 / 43

Neural Network Forecasting of Water Levels along the Texas Gulf Coast - PowerPoint PPT Presentation


  • 93 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Neural Network Forecasting of Water Levels along the Texas Gulf Coast' - vila


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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
  • 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
  • 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)

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
  • 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

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

slide8

TCOON Data Streams in the Corpus Christi Estuary

  • 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

slide9
Problem: 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
  • 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
  • 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
  • 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
  • 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

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

radbas

tansig

purelin

logsig

training of a neural network
Training of a Neural Network

Philippe Tissot - 2000

persistence model
Persistence Model
  • 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

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
  • 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

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

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

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)
slide27

 Packery Channel

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
  • 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
  • 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

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)
slide35

Storm Name

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

slide36

Isidore

Landfall 9/26/2002, near New Orleans

slide37

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

slide38
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
  • 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
  • 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.
histogram
Histogram

(amount of errors vs. location of error)