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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.

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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 Artificial Neural Networks for Water Level Forecasts

N

R

http://lighthouse.tamucc.edu


Texas coastal ocean observation network tcoon
Texas Coastal Ocean Observation Network (TCOON) Artificial Neural Networks for Water Level Forecasts

  • 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
Typical TCOON station Artificial Neural Networks for Water Level Forecasts

  • Wind Anemometer

  • Radio Antenna

  • Satellite Transmitter

  • Solar Panels

  • Data Collector

  • Water Level Sensor

  • Water Quality Sensor

  • Current Meter


Tides and water levels
Tides and water levels Artificial Neural Networks for Water Level Forecasts

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 Artificial Neural Networks for Water Level Forecasts

  • 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 t

H0 = datum offset

ac = frequency (speed) of constituent t

fy,c ey,c = node factors/equilibrium args

Hc = amplitude of constituent c

kc = phase offset for constituent c

Maximum number of constituents = 37


What we are trying to do
What we are trying to do... Artificial Neural Networks for Water Level Forecasts

We know what happens in the past...

…what will happen next?


Harmonic vs actual when it works
Harmonic vs. actual (when it works) Artificial Neural Networks for Water Level Forecasts

(coastal station)

Summertime


Harmonic vs. actual (when it fails) Artificial Neural Networks for Water Level Forecasts

Tropical Storm Season

Tropical Storm Season

(shallow bay)

(deep bay)

Frontal Passages

Frontal Passages

Summer

Summer


Standard suite used by u s national ocean service nos
Standard Suite Used by U.S. National Ocean Service (NOS) Artificial Neural Networks for Water Level Forecasts

  • 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


Tide performance along the texas coast 1997 2001
Tide performance along the Texas coast (1997-2001) Artificial Neural Networks for Water Level Forecasts

RMSE=0.16

CF=70.09

RMSE=0.16

CF=71.65

RMSE=0.15

CF=74.37

RMSE=0.12

CF=82.71

RMSE=0.12

CF=81.7

RMSE=0.10

CF=89.1


Importance of the problem
Importance of the problem Artificial Neural Networks for Water Level Forecasts

  • 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 Intracoastal Waterways

  • Worldwide increases in vessel draft

  • Galveston is the 2nd largest port in US


Artificial neural network ann modeling
Artificial Neural Network (ANN) modeling Artificial Neural Networks for Water Level Forecasts

  • 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
ANN schematic Artificial Neural Networks for Water Level Forecasts

Water Level History

 (X1+b1)

 (a1,ixi)

 (X3+b3)

Wind Squared History

b1

 (a3,ixi)

H (t+i)

b3

Water Level Forecast

Tidal Forecasts

 (a2,ixi)

 (X2+b2)

b2

Input Layer

Hidden Layer

Output Layer

Philippe Tissot - 2000


Why ann s
Why ANN’s? Artificial Neural Networks for Water Level Forecasts

  • Modeled after human brain

  • Neurons compute outputs (forecasts) based on inputs, weights and biases

  • Able to model non-linear systems


Hypothesis
Hypothesis… Artificial Neural Networks for Water Level Forecasts

  • 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
Composite Training Sets Artificial Neural Networks for Water Level Forecasts

  • 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
Artificial Neural Network setup Artificial Neural Networks for Water Level Forecasts

  • ANN models developed within the Matlab and Matlab NN Toolbox environment

  • Found simple ANNs are optimum

  • Use of ‘tansig’ and ‘purelin’ functions

  • Use of Levenberg-Marquardt training algorithm

  • ANN trained over fourteen 30-day sets of hourly data


Transform functions
Transform Functions Artificial Neural Networks for Water Level Forecasts

Purelin

Tansig

y = x

y =

(ex – e-x)/(ex + e-x)


Research location
Research Location Artificial Neural Networks for Water Level Forecasts

Primary Station

Secondary Stations


Optimization training process
Optimization (training) process Artificial Neural Networks for Water Level Forecasts

  • Used all data sets in training to find best combination of previous water levels and wind data

  • Ranked data set individual performance

  • Successively added data sets from most successful to worst to investigate performance

  • Changed forecast hours to assess trend


Ann model
ANN Model Artificial Neural Networks for Water Level Forecasts

  • Primary Station: Morgan’s Point

    • 48 Hours of previous WL

    • 36 Hours of previous winds

  • Secondary Station: Point Bolivar

    • 24 Hours of previous WL

    • 24 Hours of previous winds


Example data set
Example data set Artificial Neural Networks for Water Level Forecasts

(Julian Days) 2003265 - 2003295


Training with one set x 15cm
Training with one set (X = 15cm) Artificial Neural Networks for Water Level Forecasts

Morgan’s Point


Data set ranking
Data set ranking Artificial Neural Networks for Water Level Forecasts


Effects of increasing data sets morgan s point
Effects of increasing data sets Artificial Neural Networks for Water Level Forecasts(Morgan’s Point)

NOS Standard


Performance applied to 1998
Performance applied to 1998 Artificial Neural Networks for Water Level Forecasts

Water level (m)

Hours (1998)


Close up
Close up… Artificial Neural Networks for Water Level Forecasts

WL (m)

Hours (1998)


Model comparison
Model Comparison Artificial Neural Networks for Water Level Forecasts


Forecast trend
Forecast trend Artificial Neural Networks for Water Level Forecasts

Morgan’s Point

NOS Standard


Conclusions
Conclusions Artificial Neural Networks for Water Level Forecasts

  • Large difference in performance due to training sets

  • Increasing the number of data sets increases performance


Future direction
Future Direction Artificial Neural Networks for Water Level Forecasts

  • Analyze environmental factors of successful training sets

  • Research significance of subtle differences in ANN model training

  • Web-based predictions


The end
The End! Artificial Neural Networks for Water Level Forecasts

  • Acknowledgements:

    • General Land Office

    • Texas Water Devel. Board

    • US Corps of Eng

    • Nat'l Ocean Service

    • NASA Grant # NCC5-517

  • Division of Nearshore Research (DNR)

    • http://lighthouse.tamucc.edu


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