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The defense date of the thesis is 2005-03-11
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URN etd-0311105-135933 Statistics This thesis had been viewed 1957 times. Download 14 times. Author Shu-Fang Wu Author's Email Address No Public. Department Computer Science and Enginerring Year 2004 Semester 1 Degree Master Type of Document Master's Thesis Language English Page Count 70 Title The Application of Back-Propagation Network to Constructing the Relational Model Between Precipitation and Inundation Keyword Precipitation Neural Network Inundation Grey Relational Analysis Grey Model Back-Propagation Model Back-Propagation Model Grey Model Grey Relational Analysis Inundation Neural Network Precipitation Abstract Taipei is the political, economic and cultural center of Taiwan, but the geological structure of Taipei basin is not good as flood disaster causes unexpected loss of life and damage to citizens’ property. In recent years, the intense development of metropolitan cities in countries, worldwide, has shortened the rainfall accumulation time and increased the runoff coefficients of rainfall discharge. Along with the change in global weather and the environment, the original protection standards for drain and flood prevention facilities are comparatively reduced. The government and the private sector invest improvement observing and warning system with diligence and a large amount of fund and manpower to reduce the frequency of flood issues.
In this thesis, we collect 19 Taipei regional precipitation stations data from 1998~2004. By utilizing the learning capability of back-propagation neural networks, we can predict the precipitation data and relation of inundation regions.
We also apply the grey relational method to extracting the more influential factors for inundation. The extracted factors are then become the inputs of back-propagation network to expedite the learning process. Not only the simulation results are provided, but also a detailed discussion about the false prediction is given. The presented work can be used as a reference for analyzing the occurrence of inundation due to heavy precipitation.
Advisor Committee Yo-Ping Huang - advisor
M.S. Horng - co-chair
Shang-Lin Hsieh - co-chair
Files Date of Defense 2005-01-29 Date of Submission 2005-03-11