Developing a Goodness Criteria for Tide Predictions Based on Fuzzy Preference Ranking

Sadovski, Alexey; Steidley, Carl; Michaud, Patrick; Tissot, Philippe

doi:10.1007/3-540-45034-3_40

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…Forward and backward linear regression are applied to complete the water level data sets. Other current work involves further development of the multivariate statistical model and comparisons at various locations along the Texas Gulf coast with harmonic analysis and other forecasting methodologies such as the persistence and neural network models (Sadovski et al, 2003a). Future work will generalize the forecast capabilities to points along the Texas coast that lie between TCOON stations.…”

Section: Further Model Developmentmentioning

confidence: 99%

Real Time Web Availability of Statistical Models for Water Levels Along the Texas Coastline

Sadovski¹,

Steidley²,

Tissot³

et al. 2005

Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics

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Water level forecasts are essential to the success of trade and industry in the Gulf of Mexico, but present forecasting methodologies do not provide accurate predictions for the Gulf Coast region. Tide charts produced by harmonic analysis are the existing standard, but these charts only show the effect of astronomical forces acting upon the water. While this proves to be an accurate predictor for most of the Atlantic and Pacific Coasts, water level changes along the Texas Coast are strongly effected by meteorological factors and thus require a modified prediction model, rather than harmonic analysis alone. A web-based tool was created that combines harmonic analysis with multivariate statistical modeling to predict water levels along the Texas Gulf Coast. The result is a substantial improvement on the current model with forecasts available via the World Wide Web.

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Section: Further Model Developmentmentioning

confidence: 99%

Real Time Web Availability of Statistical Models for Water Levels Along the Texas Coastline

Sadovski¹,

Steidley²,

Tissot³

et al. 2005

Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics

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“…Deciding upon "goodness" criteria for multi-objective decision making would also be made easier if we apply these rating methods of preference ranking. For example, we are using the program to rank experts' preferences for certain criteria from National Ocean Service skill assessment statistics used for evaluation of water level forecasts [4], [7], which are based on real-time data provided by Texas Coastal Ocean Observation Network.…”

Section: Applicationsmentioning

confidence: 99%

“…It is shown also that all contemporary rating systems, (for instance those used in sports classifications), are congruent in the sense of producing the same final preference ranking. Such rating methods of preference ranking are very useful for solving multi-criteria problems of decision making and/or developing one goodness criteria from many different criteria [4].…”

Section: Introductionmentioning

confidence: 99%

Program implementation of the Rating Methods of preference ranking

Sadovski

Steidley

Torres-Knott

2005

RMTA

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Tide charts, based upon harmonic analysis, is the general method of choice for predicting water levels. In the shallow waters of the Gulf of Mexico, however, tide charts are woefully inadequate for the prediction of water levels. We have developed a number of models for the prediction of water levels. In this paper we summarize these methods and discuss the development of an axiomatic tool that we use to measure the quality of predictions of water levels in the estuaries and shallow waters of the Gulf of Mexico. This quality measure is based upon the preference rankings of National Ocean Service criteria by experts in the field.

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“…Tide charts are mostly based on astronomical forcing or the influence on water levels of the respective motions of the earth, the moon, and the sun. There are locations around the world, including the Gulf of Mexico, where other factors such meteorological forcing often dominate tidal forcing [12] and limit significantly the application of tide charts. In such cases other models must be developed to accurately forecast water levels.…”

Section: Statistical Modelingmentioning

confidence: 99%

“…Such a step by step approach produced quite good predictions. Table 1 below presents statistical data for the differences between predicted and real levels of water for 6, 12, 18, 24, 30, 36, 42, and 48 hours [12].…”

Section: Statistical Modelingmentioning

confidence: 99%

Supplemental data acquisition tools for modeling environmental systems

Steidley

Bachnak

Sadovski

2005

RMTA

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The goal of our on-going research is to develop effective and reliable tools for modeling the environmental systems of the Gulf of Mexico. For example, our on-going research into methodologies for the prediction of water levels in the shallow waters of the bays and estuaries along the Texas Gulf coast. In addition to a short description of our major data acquisition system for our research efforts, this paper presents design issues, development issues, and test results encountered in the production of two supplemental data acquisition systems as well as several of our environmental systems modeling efforts at Texas A&M University Corpus Christi.Keywords: Data acquisition, environmental modeling, robotics. ResumenEl objetivo de nuestra investigación es desarrollar herramientas eficaces y confiables para modelar los sistemas ambientales en el Golfo de México. Por ejemplo, nuestra investigaci 'on actual en metodologías para la predicción del nivel del agua en las aguas poco profundas de las bahías y estuarios a lo largo de la costa del Golfo de Texas. Nuestro *

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Developing a Goodness Criteria for Tide Predictions Based on Fuzzy Preference Ranking

Cited by 4 publications

References 11 publications

Real Time Web Availability of Statistical Models for Water Levels Along the Texas Coastline

Real Time Web Availability of Statistical Models for Water Levels Along the Texas Coastline

Program implementation of the Rating Methods of preference ranking

Supplemental data acquisition tools for modeling environmental systems

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