Storm Surges along the Gulf of

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19 Οκτ 2013 (πριν από 3 χρόνια και 5 μήνες)

70 εμφανίσεις


Neural Network Forecasting of
Storm Surges along the Gulf of
Mexico


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

*

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

* *

Civil Engineering Department, Texas A&M University

Presentation Outline


Frontal Passages and the Texas Gulf Coast


Need for better Water Level Forecasting
Models


Application of NN Modeling to Water Level
Forecasting


Model Performance


Conclusions

Frontal Systems and the Texas
Coast


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

Galveston

Study Location: Galveston, Texas

Computed Harmonic Tidal Sea Level

at Pleasure Pier, Galveston (Spring 97)

Comparison of Actual and Computed Sea
Level (Spring 97)

Importance 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
Driven Coastal Water Bodies


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


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

TCOON Stations

Primary Water Level

Water Temperature

Wind Speed

Wind Gust

Wind Direction

Typical TCOON

station page

Streaming Data 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


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

Streaming Data 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


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


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


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


Make water level forecasts using a Static
Neural Network Model

Wind Stress Factor in Water Level
Changes / Forcing Functions

Neural Network Forecasting of
Water Levels

Philippe Tissot
-

2000

H (t+i)

Output Layer

Hidden Layer

Wind Stress
History

Water Level
History

Barometric
Pressure History

Wind Stress
Forecast

Input Layer

Water Level
Forecast



(a
1
,i
x
i
)

b
1

b
2



(X
1
+b
1
)

b
3



(X
2
+b
2
)



(X
3
+b
3
)



(a
2
,i
x
i
)



(a
3
,i
x
i
)

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 Model

Performance index E



H
i

are the water levels observations and X
i

the
water level forecasts

Performance Analysis of the
Model


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
Forecasting Time

Conclusions


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
Forecasting of Storm
Surges along the Gulf of
Mexico

Presentation End

Simulated Wind Forecast using
Gaussian filter

Observed

Simulated

NWS Predictions and TCOON Observations

(Actual Forecast)

Galveston Pleasure Pier, 1999 12 Hr Predictions

Training of a Neural Network

Philippe Tissot
-

2000

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:

Differential atmospheric pressures

Wind

Precipitations

Riverine inputs

Evaporation

Changes in density

Salinity Changes

Forecasted Water Levels vs.
Observed Water Levels

RMS Error: 1ft

Neural Network Forecasts

Tidal Forecasts

RMS Error: 3ft

Comparison During Frontal
Passages

RMS Error: 1ft

Neural Network Forecasts

Tidal Forecasts

RMS Error: 3ft