Title page for etd-0708109-133013


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URN etd-0708109-133013
Author Yu-hua chuag
Author's Email Address No Public.
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Department Marine Environment and Engineering
Year 2008
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Coastline Simulation Using Fractal
Date of Defense 2009-06-02
Page Count 101
Keyword
  • Coastline Forecast
  • Fractal
  • Digital Image Processing
  • Nerual Network
  • Multiple Linear Regression Equation
  • Abstract Fractal was first used in measuring the length of the coastline, with the fractal
    research and development, not only to break the traditional Archimedean geometry,
    but also to explain many scientific to ignore the complexity and nature of nonlinear
    phenomena structure .Fractal has been widely applied to such as physics, astronomy,
    geography and sociology and other fields, as a wave of interdisciplinary research in
    recent years. Coastal areas has always been cultural, economic and activities areas
    since ancient times. Coastal zone was land and sea for the interaction region by a
    variety of factors (ex: waves, tides, currents and wind, etc.) continue to function,
    derived from different coastal terrain. Therefore changes in the coast of the deep
    impact of humanity. Under the principle of the conservation and development,
    Coastal areas should be use of modern technology to prediction, analysis, assessment,
    planning, and management, so that a sustainable preservation of coastal resources.
    In this study, static and dynamic predict and simulation the coast shape base on
    fractal. The static part is observation of 29 beaches in South China coast. And collect
    and calculate the parameters and fractal dimensions of the coast. Through the shape of
    image processing and analysis of information, to find two generators of the coast.
    Through the data mining technology to identify the criteria for classification, and to
    simulation the coastline by generate iterations method. The dynamic part is based on
    hydraulic model’s results, the use of traditional multiple linear regression and neural
    network to compare the dynamic prediction of the coastline. The results show that the
    use of neural networks to predict than the use of multiple linear regression, and effect
    of use difference angle (θ) to predict sub-coastlines than the effect of not use
    difference angle (θ) to predict, and add fractal dimension can effectively reduce the
    predict error and increase the degree of interpretation.
    Advisory Committee
  • Tzong-Yeang Lee - chair
  • Meng-Tsung Lee - co-chair
  • Rong-Chung Hsu - co-chair
  • Yang-Chi Chang - advisor
  • Files
  • etd-0708109-133013.pdf
  • indicate accessible in a year
    Date of Submission 2009-07-08

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