z. bowles, p. tissot, p. michaud, a. sadovski, s. duff, g. jeffress
DESCRIPTION
Composite Training Sets: Enhancing the Learning Power of Artificial Neural Networks for Water Level Forecasts. Z. Bowles, P. Tissot, P. Michaud, A. Sadovski, S. Duff, G. Jeffress Texas A&M University – Corpus Christi Division of Nearshore Research. D N R. http://lighthouse.tamucc.edu. - PowerPoint PPT PresentationTRANSCRIPT
Composite Training Sets: Enhancing the Learning Power of Artificial Neural Networks for Water Level Forecasts
Z. Bowles, P. Tissot, P. Michaud, A. Sadovski, S. Duff, G. Jeffress
Texas A&M University – Corpus Christi
Division of Nearshore Research
Texas Coastal Ocean Observation Network
(TCOON)Started 1988
Over 50 stations
Source of study data
Primary sponsors General Land Office Water Devel. Board US Corps of Eng Nat'l Ocean Service
Morgan’s Point
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
Tides and water levels
Tide: The periodic rise and fall of a body of water resulting from gravitational interactions between Sun, Moon, and Earth.
Tide and Current Glossary, National Ocean Service, 2000
Water Levels: Astronomical + Meteorological forcing + Other effects
Harmonic analysis
Standard method for tide predictions
Represented by constituent cosine waves with known frequencies based on gravitational (periodic) forces
Elevation of water is modeled as
h(t) = H0 + Hc fy,c cos(act + ey,c – kc)
h(t) = elevation of water at time tH0 = datum offsetac = frequency (speed) of constituent tfy,c ey,c = node factors/equilibrium args
Hc = amplitude of constituent ckc = phase offset for constituent cMaximum number of constituents = 37
Harmonic vs. actual (when it fails)
Frontal Passages
Tropical Storm Season
Summer
(shallow bay)
Frontal Passages
Tropical Storm Season
Summer
(deep bay)
Standard Suite Used by U.S. National Ocean
Service (NOS)
Central Frequency (15cm) >= 90%
Positive Outlier Frequency(30cm) <= 1%
Negative Outlier Frequency(30cm) <= 1%
Maximum Duration of Positive Outliers (30cm) - user based
Maximum Duration of Negative Outliers (30cm) - user based
RMSE=0.12CF=82.71
RMSE=0.16 CF=70.09
RMSE=0.10 CF=89.1
RMSE=0.12 CF=81.7
RMSE=0.16 CF=71.65
RMSE=0.15 CF=74.37
Tide performance along the Texas coast (1997-2001)
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 BayLarge number of barge groundings along the Texas Intracoastal Waterways Worldwide increases in vessel draftGalveston is the 2nd largest port in US
Artificial Neural Network (ANN) 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
ANN schematic
Philippe Tissot - 2000
H (t+i)
Output LayerHidden Layer
Wind Squared History
Water Level History
Input Layer
Water Level Forecast
(a1,ixi)
b1
b2
(X1+b1)
b3
(X2+b2)
(X3+b3)
(a2,ixi)
(a3,ixi)
Tidal Forecasts
Why ANN’s?
Modeled after human brain
Neurons compute outputs (forecasts) based on inputs, weights and biases
Able to model non-linear systems
Hypothesis…
If the human brain learns best when faced with many situations and challenges, so should an Artificial Neural Network
Therefore, create many challenging training sets to optimize learning patterns and situations
Composite Training Sets
Past models were trained on averaged yearly data sets
These models were trained on specific weather events and patterns of 30 days
The goal was to see the effects of specialized sets on learning and performance of the ANN
Artificial Neural Network setup
ANN models developed within the Matlab and Matlab NN Toolbox environmentFound simple ANNs are optimumUse of ‘tansig’ and ‘purelin’ functionsUse of Levenberg-Marquardt training algorithmANN trained over fourteen 30-day sets of hourly data
Transform Functions
-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
-3 -2 -1 0 1 2 3-3
-2
-1
0
1
2
3
Tansig Purelin
y = xy = (ex – e-x)/(ex + e-x)
Optimization (training) process
Used all data sets in training to find best combination of previous water levels and wind dataRanked data set individual performanceSuccessively added data sets from most successful to worst to investigate performanceChanged forecast hours to assess trend
ANN Model
Primary Station: Morgan’s Point48 Hours of previous WL36 Hours of previous winds
Secondary Station: Point Bolivar24 Hours of previous WL24 Hours of previous winds
Conclusions
Large difference in performance due to training sets
Increasing the number of data sets increases performance
Future Direction
Analyze environmental factors of successful training sets
Research significance of subtle differences in ANN model training
Web-based predictions