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Intelligent and adaptive water level prediction in texas coastal waters
Ray Bachnak
, Carl Steidley
, Alex Sadovski
, Phillipe Tissot
, Zack Bowles
School of Science, Engineering & Technology (Harrisburg)
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Dive into the research topics of 'Intelligent and adaptive water level prediction in texas coastal waters'. Together they form a unique fingerprint.
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Keyphrases
Artificial Neural Network
25%
Artificial Neural Network Model
75%
Barometric Pressure
25%
Best Predictor
25%
Central Frequency
25%
Choice Method
25%
Coastal Area
25%
Coastal Ocean Observations
25%
Coastal Waters
100%
Corpus Christi
50%
Embayment
100%
Gulf of Mexico
25%
Harmonic Analysis
50%
Historical Records
25%
Meteorological Data
25%
Modeling Techniques
25%
Network Database
25%
Ocean Observing Systems
25%
Ocean Services
100%
Persistence Model
25%
Service Use
25%
Texas
100%
Texas A&M University
25%
Texas Coast
25%
United States
25%
Water Level
75%
Water Level Data
25%
Wind Direction
25%
Wind Forcing
25%
Wind Speed
25%
Engineering
Artificial Neural Network
66%
Artificial Neural Network Model
100%
Barometric Pressure
33%
Central Frequency
33%
Gulf of Mexico
33%
Harmonics
66%
Persistence Model
33%
Shallower
100%
Earth and Planetary Sciences
Artificial Neural Network
83%
Atmospheric Pressure
16%
Coastal Region
16%
Coastal Water
100%
Gulf of Mexico
16%
Harmonics
33%
Texas
100%
Time Series
16%
United States
16%
Wind Direction
16%
Wind Forcing
16%
Wind Velocity
16%
Agricultural and Biological Sciences
Coastal Water
100%
Meteorological Data
20%
Neural Network
100%
Wind Direction
20%
Wind Speed
20%