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Data Mining Model for Beach Profiles Evolutions
|Contributors: ||NTOU:Department of Marine Environmental Informatics|
beach evolution;data mining;association analysis;decision tree;cluster analysis;artificial neural network
|Issue Date: ||2013-10-07T02:28:36Z
Abstract:The topography or morphology of a beach is influenced by meteorological and oceanic forces as well as the supply of sand. Thus, beaches constantly change their shapes. It is still difficult to predict the evolution of beaches. The research team lead by Professor Song Shan Hsiao has done beach monitoring at the Yenliao beach on the northern coast since 2004. They found that the beach is at a dynamic equilibrium. Their data seem to suggest that the changes in the beach profiles and beach width were due to seasonal effects as well as the actions of typhoons. It is important to know how the beach changes with what external parameters and how we can predict its change. Although numerical modeling can be used to simulate changes in the beach topography, there are still some difficulties for the numeral methods to work right. One way to answer these questions is to use data mining techniques, and this is the objective of this proposed study. We will collect data for the profiles of the Yenliao beach, which Professor Hsiao’s team has obtained since 2004 and will continue to measure, and other relevant information such as meteorological data, off-shore wave data, and Shunshi river flow and sediment load data to determine which parameters have strong correlations with changes of beach profile and erosion or accretion. Then we will attempt to find a predictive model for the beach evolution. This is a three-year project. In the first year, relevant data will be collected and dominant factors affecting the beach profile changes will be identified by association rules. These will be the input factors for our predictive model. In the second year, we will study how the various typhoon parameters (tracks, center pressure, etc.) effect the changes in the beach, so that a predictive model for the typhoon effects on the beach can be established. In the third year we will use factors found by association rules with appropriate conversions as the main input factors to build a predictive model for the beach evolution based on data mining techniques. We have tested our data mining technique on building a model for the variation of Fulong beach area. Our initial results showed that using decision tree we can obtain some satisfactory results. This means that the method proposed in this study is a viable one for building predictive models for changes in beach profile or topography.
|Appears in Collections:||[海洋環境資訊系] 研究計畫|
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