The Application of Back-Propagation Network to Constructing the Relational Model Between Precipitation and Inundation

• Main Authors: Shu-Fang Wu, 吳淑芳
• Other Authors: Yo-Ping Huang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/17099316722637466306

碩士 === 大同大學 === 資訊工程學系(所) === 93 === 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.