Transcript tissot-cfw-jan30.ppt

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
 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
• 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?
Comparison of Tide Charts and
Measured Water Levels (CCNAS 1998)
Water Level
(m)
2
TCOON Measurements
Harmonic Forecasts
1
(a)
0
Wind Pseudostress Water Anomaly
[m2/s2]
(m)
0
50
100
150
200
250
300
350
1
0.5
(b)
0
-0.5
0
50
100
150
200
250
300
350
600
400
(c)
200
0
0
50
100
150
200
Julian Day,1998
250
300
350
Tidal Charts Performance along
the Texas Coast (1997-2001)
Sab. RMSE=0.16
CF=70.09
Pleasure Pier
RMSE=0.16
Pier 21
CF=71.65
RMSE=0.15
BHP RMSE=0.12 CF=74.37
CF=82.71
Coast Guard RMSE=0.12
CF=81.7
Port Isab. RMSE=0.10
CF=89.1
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
Study Area: Corpus Christi Estuary
Port Aransas
Nueces Bay
Aquarium
Ingleside
Corpus Christi
Bay
Oso Bay
Port of Corpus Christi
Naval Air Station
Packery Channel
Bob Hall Pier
Gulf of
Mexico
TCOON Data Streams in the Corpus
Christi Estuary
6 TCOON Stations
Measuring:
• Water levels (6)
Port Aransas
Nueces Bay
Aquarium
Ingleside
• Wind directions (4)
Corpus Christi
Bay
Naval Air Station
Port of Corpus Christi
Oso Bay
• Wind speeds (4)
Gulf of
Mexico
Packery Channel
Bob Hall Pier
 10 x 8760 hourly
measurements per year
• Barometric pressure
• Air temperature
• Water temperature
• 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
 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 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
• 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
Comparison of Tide Charts and
Measured Water Levels (CCNAS 1998)
Water Level
(m)
2
TCOON Measurements
Harmonic Forecasts
1
(a)
0
Wind Pseudostress Water Anomaly
[m2/s2]
(m)
0
50
100
150
200
250
300
350
1
0.5
(b)
0
-0.5
0
50
100
150
200
250
300
350
600
400
(c)
200
0
0
50
100
150
200
Julian Day,1998
250
300
350
Neural Network Forecasting of
Water Levels
Water Level
History
Wind Stress
History
Wind Stress
Forecast
Barometric
Pressure History
Input Layer
 (a1,ixi)
 (X1+b1)
 (X3+b3)
 (a3,ixi)
b1
 (a2,ixi)
b3
 (X2+b2)
H (t+i)
Water Level
Forecast
b2
Hidden Layer
Output Layer
Philippe Tissot - 2000
Activation Functions
y = radbas(x)
y = tansig(x)
1
1
0.8
radbas
tansig
0.6
0.8
0.4
0.6
0.2
0.4
-0.2
0
ye
 x2
-0.4
0.2
-0.6
-0.8
0
-1
-3
-2
-1
0
1
2
3
-3
-2
-1
0
1
2
3
e x  ex
y x
e  ex
y = logsig(x)
y = purelin(x)
3
1
purelin
logsig
2
0.8
1
yx
0.6
1
y
1  ex
0
0.4
-1
0.2
-2
0
-3
-3
-2
-1
0
1
2
3
-3
-2
-1
0
1
2
3
Training of a Neural Network
Philippe Tissot - 2000
Error Surface
CCNAS ANN 24-hour Forecasts for
1997 (ANN trained over 2001 Data Set)
Water Levels (m)
1
0.5
0
-0.5
0
50
100
150
200
Julian Day,1997
250
300
350
400
CCNAS ANN 24-hour Forecasts for
1997 (ANN trained over 2001 Data Set)
0.7
0.6
Water Levels (m)
0.5
0.4
0.3
0.2
0.1
0
75
80
85
90
95
100
105
Julian Day,1997
110
115
120
125
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
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
• 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
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
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)
Tide
Tables
ANN
Model
Tide
Tables
ANN
Model
Average error
(bias)
-2.7 
2.9 cm
-0.4 
1.7 cm
Average
(bias)
-2.6 
2.4
-0.1 
1.1 cm
Average
Absolute error
8.9 
1.5 cm
6.0 
0.6 cm
Average
Absolute error
8.5 
1.5 cm
4.5 
0.4 cm
Normalized
RMS error
0.29 
0.05
0.20 
0.02
Normalized
RMS error
0.40 
0.05
0.21 
0.01
POF (15 cm)
4.5% 
1.9%
2.6% 
1.3%
POF (15 cm)
4.8% 
1.1%
0.9%0.
4%
NOF (15 cm)
12.8%
6.8%
3.8%
2.6%
NOF (15 cm
11.4%
5.6%
1.3%1.
4%
MDPO
cm)
(15
67  25
hrs
24  7
hrs
MDPO (15 cm)
103 
31 hrs
19  6
hrs
MDNO
cm)
(15
103 
67 hrs
39  34
hrs
MDNO (15 cm)
20517
7 hrs
29  33
hrs
BHP
CCNAS
error
Comparison of ANN & Harmonic Forecasts
for 24 Hour Forecasts (’97-’01)
Tide
Tables
ANN
Model
Tide
Tables
ANN
Model
-2.4 
2.6 cm
-0.2 
1.3 cm
Average
(bias)
-2.6 
2.2 cm
-0.2

0.8 cm
Average
Absolute error
8.4 
1.4 cm
5.2 
0.5 cm
Average
Absolute error
7.6 
1.6 cm
3.5

0.4 cm
Normalized
RMS error
0.31 
0.05
0.19 
0.02
Normalized
RMS error
0.45 
0.07
0.21
0.03
POF (15 cm)
4.6%
1.8%
1.8% 
0.6%
POF (15 cm)
2.6%
1.1%
0.4% 
0.3%
NOF (15 cm)
11.1%
5.9%
2.2% 
2.2%
NOF (15 cm)
9.6%
6.4%
1.0% 
1.3%
MDPO (15 cm)
74 
21 hrs
23  7
hrs
MDPO (15 cm)
77 
41 hrs
14  10
hrs
MDNO (15 cm)
123 
81 hrs
31  37
hrs
MDNO (15 cm)
2011
87 hrs
30  38
hrs
Port Aransas
Average
(bias)
error
Packery
Channel
error

ETA-12 Forecast Locations
Comparison Eta-12 Wind Forecasts /
TCOON Measurements - Bias
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
• 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
Frances Trajectory
Landfall on Sept. 11
CCNAS ANN 12-hour Forecasts During
1998 Tropical Storm Frances (ANN
trained over 2001 Data Set)
1.2
Water Levels (m)
1
0.8
0.6
0.4
0.2
0
230
235
240
245
250
255
Julian Day,1998
260
265
270
275
CCNAS ANN 24-hour Forecasts
During 1998 Tropical Storm Frances
(ANN trained over 2001 Data Set)
1.2
Water Levels (m)
1
0.8
0.6
0.4
0.2
0
230
240
250
260
Julian Day,1998
270
280
2002 Tropical Storms and
Hurricanes
Storm
Name
Landfall
Locations
Landfall
Date
Lili
Storm
Type at
Landfall
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
Isidore
Landfall 9/26/2002, near
New Orleans
Effect on Water Levels of 2002
Tropical Storms and Hurricanes
Isidore
Faye
Lili
Bertha
Faye
Isidore
Lili
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
1.2
1
Water Levels (m)
0.8
Black - measurement
Blue – Harmonic
Green – Persistent
Red - ANN
0.6
0.4
0.2
0
-0.2
220
240
260
280
Julian Day,2002
300
320
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
• 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?
Non-Linear Relationship Between Wind
Forcing and Water Level Changes
Group
Histogram (amount of errors vs. location of error)