neural network forecasting of water levels along the texas gulf coast
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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Neural Network Forecasting of Neural Network Forecasting of Water Levels along the Texas Gulf Water Levels along the Texas Gulf
CoastCoast
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
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Presentation OutlinePresentation 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
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TidesTides
• 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?
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Comparison of Tide Charts and Comparison of Tide Charts and Measured Water Levels (CCNAS 1998)Measured Water Levels (CCNAS 1998)
0 50 100 150 200 250 300 350
0
1
2
Wat
er L
evel
(m)
0 50 100 150 200 250 300 350-0.5
0
0.5
1
Wat
er A
no
mal
y(m
)
(b)
0 50 100 150 200 250 300 3500
200
400
600
Win
d P
seu
do
stre
ss[m
2/s2
]
Julian Day,1998
TCOON MeasurementsHarmonic Forecasts
(a)
(c)
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Tidal Charts Performance along Tidal Charts Performance along the Texas Coast (1997-2001)the Texas Coast (1997-2001)
BHP RMSE=0.12 CF=82.71
Sab. RMSE=0.16 CF=70.09
Port Isab. RMSE=0.10 CF=89.1
Coast Guard RMSE=0.12 CF=81.7
Pleasure Pier RMSE=0.16 CF=71.65Pier 21
RMSE=0.15 CF=74.37
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Water Level Changes and TidesWater 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
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Study Area: Corpus Christi EstuaryStudy Area: Corpus Christi Estuary
Bob Hall Pier
Packery Channel
Naval Air Station
AquariumIngleside
Port AransasNueces Bay
Corpus Christi Bay
Gulf of Mexico
Oso Bay
Port of Corpus Christi
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TCOON Data Streams in the Corpus TCOON Data Streams in the Corpus Christi EstuaryChristi Estuary
Bob Hall Pier
Packery Channel
Naval Air Station
Aquarium InglesidePort Aransas
Nueces Bay
Corpus Christi Bay
Gulf of Mexico
Oso BayPort of Corpus Christi
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
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• 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
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Data Intensive ModelingData 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
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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
Data Intensive ModelingData Intensive Modeling
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Neural Network ModelingNeural 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
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Neural Network FeaturesNeural Network Features
Non linear modeling capability
Generic modeling capability
Robustness to noisy data
Ability for dynamic learning
Requires availability of high density of data
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Comparison of Tide Charts and Comparison of Tide Charts and Measured Water Levels (CCNAS 1998)Measured Water Levels (CCNAS 1998)
0 50 100 150 200 250 300 350
0
1
2
Wat
er L
evel
(m)
0 50 100 150 200 250 300 350-0.5
0
0.5
1
Wat
er A
no
mal
y(m
)
(b)
0 50 100 150 200 250 300 3500
200
400
600
Win
d P
seu
do
stre
ss[m
2/s2
]
Julian Day,1998
TCOON MeasurementsHarmonic Forecasts
(a)
(c)
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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
(a1,ixi)
b1
b2
(X1+b1)
b3
(X2+b2)
(X3+b3)
(a2,ixi)
(a3,ixi)
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Activation FunctionsActivation Functions
xx
xx
ee
eey
2xey
xy
xey
1
1
radbas tansig
logsigpurelin
-3 -2 -1 0 1 2 3
0
0.2
0.4
0.6
0.8
1
y = radbas(x)
-3 -2 -1 0 1 2 3
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
y = tansig(x)
-3 -2 -1 0 1 2 3
0
0.2
0.4
0.6
0.8
1
y = logsig(x)
-3 -2 -1 0 1 2 3-3
-2
-1
0
1
2
3y = purelin(x)
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Training of a Neural NetworkTraining of a Neural Network
Philippe Tissot - 2000
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Error SurfaceError Surface
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CCNAS ANN 24-hour Forecasts for CCNAS ANN 24-hour Forecasts for 1997 (ANN trained over 2001 Data Set)1997 (ANN trained over 2001 Data Set)
0 50 100 150 200 250 300 350 400-0.5
0
0.5
1
Wat
er L
evel
s (m
)
Julian Day,1997
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75 80 85 90 95 100 105 110 115 120 125
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Wat
er L
evel
s (m
)
Julian Day,1997
CCNAS ANN 24-hour Forecasts for CCNAS ANN 24-hour Forecasts for 1997 (ANN trained over 2001 Data Set)1997 (ANN trained over 2001 Data Set)
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Persistence ModelPersistence 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
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Performance MeasurementsPerformance MeasurementsAverage 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.
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Performance Analysis of the Performance Analysis of the Model for BHP and CCNASModel 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
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BHP Performance AnalysisBHP Performance Analysis
0.00
0.02
0.04
0.06
0.08
0.10
0 hr 6 hr 12 hr 18 hr 24 hr 30 hr 36 hr 42 hr 48 hr 54 hr
Forecasting Period
harmonic forecasts (blue/squares), Persistence model (green/diamonds), ANN model without wind forecasts (red dashed/triangles) and ANN model with wind forecasts
(red/circles)
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CCNAS Performance AnalysisCCNAS Performance Analysis
0.00
0.02
0.04
0.06
0.08
0.10
0 hr 6 hr 12 hr 18 hr 24 hr 30 hr 36 hr 42 hr 48 hr 54 hr
Forecasting Period
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)
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Comparison of ANN & Harmonic Forecasts Comparison of ANN & Harmonic Forecasts for 24 Hour Forecasts (’97-’01)for 24 Hour Forecasts (’97-’01)
BHP
Tide Tables
ANN Model
Average error (bias)
-2.7 2.9 cm
-0.4 1.7 cm
Average Absolute error
8.9 1.5 cm
6.0 0.6 cm
Normalized RMS error
0.29 0.05
0.20 0.02
POF (15 cm) 4.5% 1.9%
2.6% 1.3%
NOF (15 cm) 12.8%6.8%
3.8%2.6%
MDPO (15 cm)
67 25 hrs
24 7 hrs
MDNO (15 cm)
103 67 hrs
39 34 hrs
CCNAS
Tide Tables
ANN Model
Average error (bias)
-2.6 2.4
-0.1 1.1 cm
Average Absolute error
8.5 1.5 cm
4.5 0.4 cm
Normalized RMS error
0.40 0.05
0.21 0.01
POF (15 cm) 4.8%
1.1%
0.9%0.4%
NOF (15 cm 11.4%5.6%
1.3%1.4%
MDPO (15 cm) 103 31 hrs
19 6 hrs
MDNO (15 cm) 205177 hrs
29 33 hrs
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Comparison of ANN & Harmonic Forecasts Comparison of ANN & Harmonic Forecasts for 24 Hour Forecasts (’97-’01)for 24 Hour Forecasts (’97-’01)
Packery Channel
Tide Tables
ANN Model
Average error (bias)
-2.6 2.2 cm
-0.2 0.8 cm
Average Absolute error
7.6 1.6 cm
3.5 0.4 cm
Normalized RMS error
0.45 0.07
0.21 0.03
POF (15 cm) 2.6%1.1%
0.4% 0.3%
NOF (15 cm) 9.6%6.4%
1.0% 1.3%
MDPO (15 cm) 77 41 hrs
14 10 hrs
MDNO (15 cm) 201187 hrs
30 38 hrs
Tide
TablesANN
Model
Average error (bias)
-2.4 2.6 cm
-0.2 1.3 cm
Average Absolute error
8.4 1.4 cm
5.2 0.5 cm
Normalized RMS error
0.31 0.05
0.19 0.02
POF (15 cm) 4.6%1.8%
1.8% 0.6%
NOF (15 cm) 11.1%5.9%
2.2% 2.2%
MDPO (15 cm) 74 21 hrs
23 7 hrs
MDNO (15 cm) 123 81 hrs
31 37 hrs
Port Aransas
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ETA-12 Forecast LocationsETA-12 Forecast Locations
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Comparison Eta-12 Wind Forecasts / Comparison Eta-12 Wind Forecasts / TCOON Measurements - BiasTCOON Measurements - Bias
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Model Assessment for non Storm Model Assessment for non Storm ConditionsConditions
• 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
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Tropical Storms and HurricanesTropical 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?
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Tropical Storm Frances - Tropical Storm Frances - September 7-17, 1998 September 7-17, 1998
Frances Trajectory
Landfall on Sept. 11
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230 235 240 245 250 255 260 265 270 2750
0.2
0.4
0.6
0.8
1
1.2
Wat
er L
evel
s (m
)
Julian Day,1998
CCNAS ANN 12-hour Forecasts During CCNAS ANN 12-hour Forecasts During 1998 Tropical Storm Frances (ANN 1998 Tropical Storm Frances (ANN
trained over 2001 Data Set)trained over 2001 Data Set)
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CCNAS ANN 24-hour Forecasts CCNAS ANN 24-hour Forecasts During 1998 Tropical Storm Frances During 1998 Tropical Storm Frances (ANN trained over 2001 Data Set)(ANN trained over 2001 Data Set)
230 240 250 260 270 280
0
0.2
0.4
0.6
0.8
1
1.2
Wate
r L
ev
els
(m
)
Julian Day,1998
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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 2002 Tropical Storms and HurricanesHurricanes
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IsidoreIsidore
Landfall 9/26/2002, near New Orleans
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Effect on Water Levels of 2002 Effect on Water Levels of 2002 Tropical Storms and HurricanesTropical Storms and Hurricanes
NAS: up to ~ + 80 cm BHP: up to ~ + 80 cm
Galveston Pleasure Pier: up to ~ + 110 cm Sabine: up to ~ + 80 cm
Bertha
FayeIsidore
LiliBertha
FayeIsidore
Lili
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Comparison of Measured and Forecasted (12-Hour) Comparison of Measured and Forecasted (12-Hour) Water levels during the 2002 Tropical Storms and Water levels during the 2002 Tropical Storms and
Hurricanes at CCNASHurricanes at CCNAS
220 240 260 280 300 320-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Wate
r L
ev
els
(m
)
Julian Day,2002
Black - measurement
Blue – Harmonic
Green – Persistent
Red - ANN
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ConclusionsConclusions• 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
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Ongoing/Future WorkOngoing/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.
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Questions?
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Non-Linear Relationship Between Wind Non-Linear Relationship Between Wind Forcing and Water Level ChangesForcing and Water Level Changes
Group
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(amount of errors vs. location of error)HistogramHistogram