Composite Training Sets: Enhancing the Learning Power of Artificial Neural Networks for Water Level ...
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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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Z. Bowles, P. Tissot, P. Michaud, A. Sadovski, S. Duff, G. Jeffress

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Z bowles p tissot p michaud a sadovski s duff g jeffress

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


Z bowles p tissot p michaud a sadovski s duff g jeffress

D

N

R

http://lighthouse.tamucc.edu


Texas coastal ocean observation network tcoon

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

Typical TCOON station

  • 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

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


Z bowles p tissot p michaud a sadovski s duff g jeffress

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 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...

We know what happens in the past...

…what will happen next?


Harmonic vs actual when it works

Harmonic vs. actual (when it works)

(coastal station)

Summertime


Z bowles p tissot p michaud a sadovski s duff g jeffress

Harmonic vs. actual (when it fails)

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)

  • 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)

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

  • 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

  • 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

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?

  • Modeled after human brain

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

  • Able to model non-linear systems


Hypothesis

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

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

Artificial Neural Network setup

  • 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

Purelin

Tansig

y = x

y =

(ex – e-x)/(ex + e-x)


Research location

Research Location

Primary Station

Secondary Stations


Optimization training process

Optimization (training) process

  • 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

  • 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

(Julian Days) 2003265 - 2003295


Training with one set x 15cm

Training with one set (X = 15cm)

Morgan’s Point


Data set ranking

Data set ranking


Effects of increasing data sets morgan s point

Effects of increasing data sets(Morgan’s Point)

NOS Standard


Performance applied to 1998

Performance applied to 1998

Water level (m)

Hours (1998)


Close up

Close up…

WL (m)

Hours (1998)


Model comparison

Model Comparison


Forecast trend

Forecast trend

Morgan’s Point

NOS Standard


Conclusions

Conclusions

  • Large difference in performance due to training sets

  • Increasing the number of data sets increases performance


Future direction

Future Direction

  • Analyze environmental factors of successful training sets

  • Research significance of subtle differences in ANN model training

  • Web-based predictions


The end

The End!

  • 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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