Philippe Tissot*, Patrick Michaud*, Daniel Cox**
description
Transcript of Philippe Tissot*, Patrick Michaud*, Daniel Cox**
Optimization and Performance of a Neural Network Model
Forecasting Water Levels for the Corpus Christi, Texas, Estuary
Philippe Tissot*, Patrick Michaud*, Daniel Cox**Philippe Tissot*, Patrick Michaud*, Daniel Cox**
*Texas A&M University-Corpus Christi, Corpus *Texas A&M University-Corpus Christi, Corpus Christi, TexasChristi, Texas
**Oregon State University, Corvallis, Oregon**Oregon State University, Corvallis, Oregon
Texas A&M University-Corpus Christi Division of Near Shore Research
Presentation Outline
Texas Coastal Ocean Observation Network Texas Coastal Ocean Observation Network (TCOON)(TCOON)
Tides and Water Levels in the Gulf of MexicoTides and Water Levels in the Gulf of Mexico Artificial Neural Network Forecasting of Water Artificial Neural Network Forecasting of Water
Levels and Application to the Corpus Christi Levels and Application to the Corpus Christi EstuaryEstuary
ANN Performance for Water Level ForecastingANN Performance for Water Level Forecasting ANN performance during a Tropical StormANN performance during a Tropical Storm Conclusions Conclusions
Texas A&M University-Corpus Christi Division of Near Shore Research
Texas Coastal Observation Network (TCOON)
Started 1988Started 1988 Over 50 stationsOver 50 stations Primary SponsorsPrimary Sponsors
General Land OfficeGeneral Land Office Water Devel. BoardWater Devel. Board US Corps of EngUS Corps of Eng Nat'l Ocean ServiceNat'l Ocean Service
Texas A&M University-Corpus Christi Division of Near Shore Research
Typical TCOON station
Wind anemometerWind anemometer Radio AntennaRadio Antenna Satellite TransmitterSatellite Transmitter Solar PanelsSolar Panels Data CollectorData Collector Water Level SensorWater Level Sensor Water Quality SensorWater Quality Sensor Current MeterCurrent Meter
Texas A&M University-Corpus Christi Division of Near Shore Research
TCOON Web Site
Texas A&M University-Corpus Christi Division of Near Shore Research
Tides and Water Levels
Tide: The periodic rise and fall of a body of Tide: The periodic rise and fall of a body of water resulting from gravitational water resulting from gravitational interactions between Sun, Moon, and Earth.interactions between Sun, Moon, and Earth.
Tide and Current GlossaryTide and Current Glossary, National Ocean Service, 2000, National Ocean Service, 2000
Water Levels: Astronomical + Meteorological Water Levels: Astronomical + Meteorological forcing + Other effectsforcing + Other effects
Texas A&M University-Corpus Christi Division of Near Shore Research
Study Site: CC Estuary
Bob Hall Pier
Packery Channel
Naval Air Station
AquariumIngleside
Port AransasNueces Bay
Corpus Christi Bay Gulf of
Mexico
Oso BayPort of Corpus Christi
Texas A&M University-Corpus Christi Division of Near Shore Research
Comparison of Tides and Water Levels
TCOON MeasurementsTide Tables
Corpus Christi Naval Air Station
Texas A&M University-Corpus Christi Division of Near Shore Research
Comparison of Tides, Water Levels, and Winds (squared)
Wa
ter
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l (m
)
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Win
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Julian Day,1997
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Texas A&M University-Corpus Christi Division of Near Shore Research
Challenge
Develop a water level forecasting model Develop a water level forecasting model that captures the non linear relationship that captures the non linear relationship between wind forcing and future water level between wind forcing and future water level changeschanges
Take advantage of the large amount of real-Take advantage of the large amount of real-time data available through TCOONtime data available through TCOON
Artificial Neural Network Model?Artificial Neural Network Model?
Texas A&M University-Corpus Christi Division of Near Shore Research
ANN Features for Water Level Forecasts
Non linear modeling capabilityNon linear modeling capability
Generic modeling capabilityGeneric modeling capability
Robustness to noisy dataRobustness to noisy data
Ability for dynamic learningAbility for dynamic learning
Requires availability of high density of dataRequires availability of high density of data
Texas A&M University-Corpus Christi Division of Near Shore Research
ANN Model
H (t+i)
Output LayerHidden Layer
Observed Winds
Observed Water Levels
Observed Barometric Pressures
Forecasted Winds
Input Layer
Water Level Forecast
(a1,ixi)
b1
b2
(X1+b1)
b3
(X2+b2)
(X3+b3)
(a2,ixi)
(a3,ixi)
Texas A&M University-Corpus Christi Division of Near Shore Research
ANNs Characterisitics
ANN models developed within the Matlab ANN models developed within the Matlab (R13) and Matlab NN Toolbox environment(R13) and Matlab NN Toolbox environment
Simple ANNs are optimumSimple ANNs are optimum Use of ‘tansig’ and ‘purelin’ functionsUse of ‘tansig’ and ‘purelin’ functions Levenberg-Marquardt training algorithmLevenberg-Marquardt training algorithm ANN Trained over 1 year of hourly data ANN Trained over 1 year of hourly data
(8750 forecasts)(8750 forecasts)
Texas A&M University-Corpus Christi Division of Near Shore Research
CCNAS ANN 24-hour Forecasts
0 50 100 150 200 250 300 350 400-0.5
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s (m
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Julian Day,1997
ANN trained over 2001 Data Set
Texas A&M University-Corpus Christi Division of Near Shore Research
CCNAS ANN 24-hour Forecasts
0 50 100 150 200 250 300 350 400-0.5
0
0.5
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Wat
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evel
s (m
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ANN trained over 2001 Data Set
Texas A&M University-Corpus Christi Division of Near Shore Research
CCNAS ANN 24-hour Forecasts
75 80 85 90 95 100 105 110 115 120 125
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Julian Day,1997
ANN trained over 2001 Data Set
Texas A&M University-Corpus Christi Division of Near Shore Research
Model Assessment
Based on five 1-year data sets: ‘97, ‘98, ’99, ’00, ‘01 Based on five 1-year data sets: ‘97, ‘98, ’99, ’00, ‘01 including observed water levels and winds, and tide including observed water levels and winds, and tide forecastsforecasts
Train the NN model using one data set e.g. ‘97 for Train the NN model using one data set e.g. ‘97 for each forecast target, e.g. 12 hourseach forecast target, e.g. 12 hours
Apply the NN model to the other four data sets, Apply the NN model to the other four data sets, Repeat the performance analysis for each training Repeat the performance analysis for each training
year and forecast target and compute the model year and forecast target and compute the model performance and variabilityperformance and variability
Texas A&M University-Corpus Christi Division of Near Shore Research
Performance Analysis(Coastal Station)
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Forecasting Period
Ave
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solu
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orec
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Err
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m]
Tides
Persistent Model
ANN model w/o Wind Forecasts
ANN model with Wind Forecasts
Texas A&M University-Corpus Christi Division of Near Shore Research
Performance Analysis(Estuary Station)
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Forecasting Period
Ave
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solu
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Err
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m]
Tides
Persistent Model
ANN model
ANN model (plus Coastal Obs.)
Texas A&M University-Corpus Christi Division of Near Shore Research
Performance Analysis(Estuary Station)
ANN inputs include Estuary and Coastal Measurements
82 %
84 %
86 %
88 %
90 %
92 %
94 %
96 %
98 %
100 %
102 %
0 10 20 30 40 50 60
Forecast Time [hrs]
Cen
tral
Fre
qu
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CF
(15
cm
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ANN with Wind Forecasts
ANN without Wind Forecasts
Persistent Model
Tides
CF(15 cm) = 90%
Texas A&M University-Corpus Christi Division of Near Shore Research
Comparison of Tides and ANN forComparison of Tides and ANN for24- Hour Forecasts24- Hour Forecasts
BHP (Coastal) Tides ANN
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 Tides ANN
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
Texas A&M University-Corpus Christi Division of Near Shore Research
Comparison of Tides and ANN forComparison of Tides and ANN for24- Hour Forecasts24- Hour Forecasts
Packery Channel
Tides ANN
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
Tides ANN
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
Texas A&M University-Corpus Christi Division of Near Shore Research
Tropical Storm Frances - September 7-17, 1998
Frances Trajectory
Landfall on Sept. 11
Texas A&M University-Corpus Christi Division of Near Shore Research
230 235 240 245 250 255 260 265 270 2750
0.2
0.4
0.6
0.8
1
1.2
Wat
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evel
s (m
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Julian Day,1998
CCNAS ANN 12-hour Forecasts
ANN trained over 1997 Data Set
CF(Tides) = 17 %CF(Persistent) = 94 %CF(NN w/o Forecasts) = 95%CF(NN with Forecasts) = 98 %
Texas A&M University-Corpus Christi Division of Near Shore Research
CCNAS ANN 24-hour Forecasts
230 240 250 260 270 280
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1
1.2
Wat
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evel
s (m
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Julian Day,1998
ANN trained over 1997 Data Set
CF(Tides) = 17 %CF(Persistent) = 92 %CF(NN w/o Forecasts) = 82%CF(NN with Forecasts) = 85 %
Texas A&M University-Corpus Christi Division of Near Shore Research
Conclusions
ANN models improve considerably on the tides ANN models improve considerably on the tides for regular conditions and frontal passagesfor regular conditions and frontal passages
Once trained computationally very efficient Once trained computationally very efficient Allow great modeling flexibilityAllow great modeling flexibility Accuracy and location of the Wind forecasts will Accuracy and location of the Wind forecasts will
determine model performance beyond 15 hoursdetermine model performance beyond 15 hours Promising for short term, up to 12 hours, water Promising for short term, up to 12 hours, water
level forecasts during stormslevel forecasts during storms
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Questions?