Neural Network Forecasting of Neural Network Forecasting of Storm Surges along the Gulf of Storm Surges along the Gulf of

MexicoMexico

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

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

Presentation OutlinePresentation Outline

ü Frontal Passages and the Texas Gulf Coast

ü Need for better Water Level Forecasting Models

ü Application of NN Modeling to Water Level Forecasting

ü Model Performance

ü Conclusions

Frontal Systems and the Texas Frontal Systems and the Texas CoastCoast

• Regular frontal passages from late September to mid May every 7 to 10 days

• Wind gusts regularly up to 40-45 mph along the coast

• Effects of the frontal passages last up to 4-5 days

• Changes in temperature and barometric pressure

• The resulting changes in water levels exceed the tidal range

GalvestonGalveston

Study Location: Galveston, TexasStudy Location: Galveston, Texas

Computed Harmonic Tidal Sea Level Computed Harmonic Tidal Sea Level at Pleasure Pier, Galveston (Spring 97)at Pleasure Pier, Galveston (Spring 97)

Comparison of Actual and Computed Sea Comparison of Actual and Computed Sea Level (Spring 97)Level (Spring 97)

Importance of the ProblemImportance of the Problem

• Gulf Coast Ports account for 52.3% of total US tonnage (1995)

• 1240 ship groundings from 1986 to 1991 in Galveston Bay

• Large number of barge groundings along the Texas Intra Coastal Waterways (ICW)

• Worldwide increases in vessel draft

Water Level Forecasting in non Tidally Water Level Forecasting in non Tidally Driven Coastal Water BodiesDriven Coastal Water Bodies

üWater level forecasting is important for a number of coastal users (ports, emergency management, recreational users, …)

üForecasting models need to account for other factors then tidal forces and therefore will necessarily be “near real time” models

TCOON StationsTCOON Stations

Primary Water LevelPrimary Water Level

Water TemperatureWater Temperature

Wind SpeedWind Speed

Wind GustWind Gust

Wind DirectionWind Direction

Typical TCOONstation pageTypical TCOONstation page

Streaming Data ModelingStreaming Data Modeling

ü Real time data availability is rapidly increasing

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

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

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

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

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

Streaming Data ModelingStreaming Data Modeling

Neural Network ModelingNeural Network Modeling

• Started in the 60’s

• Key innovation in the late 80’s: Backpropagationlearning algorithms

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

• Growing number of publications presenting environmental applications

Neural Network FeaturesNeural Network Features

ü Non linear modeling capability

ü Generic modeling capability

ü Robustness to noisy data

ü Ability for dynamic learning

ü Requires availability of high density of data

Neural Network Forecasting of Neural Network Forecasting of Water LevelsWater Levels

ü Use historical time series of previous water levels, winds, barometric pressure as input

ü Train neural network to associate changes in inputs and future water level changes

ü Make water level forecasts using a Static Neural Network Model

Wind Stress Factor in Water Level Wind Stress Factor in Water Level Changes / Forcing FunctionsChanges / Forcing Functions

Neural Network Forecasting of Neural Network Forecasting of Water LevelsWater Levels

Philippe Tissot - 2000

H (t+i)

Output LayerHidden Layer

Wind Stress History

Water Level History

Barometric Pressure History

Wind Stress Forecast

Input Layer

Water Level Forecast

Σ (a1,ixi)

b1

b2

ƒ (X1+b1)

b3

ƒ (X2+b2)

ƒ (X3+b3)

Σ (a2,ixi)

Σ (a3,ixi)

NN Model, 24 Hr prediction

Harmonic Analysis

Comparison between measured water levels (black), tidal chart forecasts (blue), and 24 hour neural network forecasts (red) for Galveston Pleasure Pier during the spring of 1999 (Cox, Tissot, Michaud). The neural network model was trained for a period of 90 days during the spring of 1997 and is applied here to a frontal passage during the spring of 1999. The accuracy of the 24 hour neural network forecast shows the ability to predict the timing and the intensity of frontal passages.

Performance of the ModelPerformance of the Model

Performance index E

( )

( )2

1

1

2

21

1

2

1

1

−

=

∑

∑

=

=

N

i i

N

i ii

HN

XHNE

Hi are the water levels observations and Xi the water level forecasts

Performance Analysis of the Performance Analysis of the ModelModel

• Spring ‘97, ‘98, ‘99 data sets covering 90 days with hourly water levels and weather data

• 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 two data sets, e.g. ‘98, ‘99

• Repeat the performance analysis for each training year and forecast target and compute the error index

Model Comparisons for Varying Model Comparisons for Varying Forecasting TimeForecasting Time

0

0.2

0.4

0.6

0.8

1

0 6 12 18 24 30 36 42 48

Forecast time [hours]

For

ecas

t P

erfo

rman

ce

Forecasts not Including Wind Forecasts

Forecasts Including Modified Wind Speed at Forecasted Time

Forecasts Including Exact Wind Speed at Forecasted Time

Tide Charts

ConclusionsConclusions

• Neural network modeling shows excellent promises for local forecasting of water levels during frontal passages (6 to 30 hour forecasts)

• 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

• Expanding the application of the model to other locations along the coast of Texas

Neural Network Neural Network Forecasting of Storm Forecasting of Storm

Surges along the Gulf of Surges along the Gulf of MexicoMexico

Presentation End

Simulated Wind Forecast using Simulated Wind Forecast using Gaussian Gaussian filterfilter

Observed

Simulated

NWS Predictions and TCOON ObservationsNWS Predictions and TCOON Observations(Actual Forecast)(Actual Forecast)

Galveston Pleasure Pier, 1999 12 Hr Predictions

Training of a Neural NetworkTraining of a Neural Network

Philippe Tissot - 2000

Water Level Changes and TidesWater Level Changes and Tides

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

ü Other factors influencing water level changes:

Salinity ChangesChanges in densityEvaporationRiverine inputsPrecipitationsWind

Differential atmospheric pressures

Forecasted Water Levels vs. Forecasted Water Levels vs. Observed Water LevelsObserved Water Levels

RMS Error: 1ft

Neural Network Forecasts Tidal Forecasts

RMS Error: 3ft

Comparison During Frontal Comparison During Frontal PassagesPassages

RMS Error: 1ft

Neural Network Forecasts Tidal Forecasts

RMS Error: 3ft