Hillslope Area Groundwater Level estimate — a BPNN approach

• Main Authors: Chung Yun-Jing, 鍾芸菁
• Other Authors: 呂建華
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/71683277443291067535

碩士 === 國立中興大學 === 土木工程學系 === 93 === Abstract This study uses six hillside watersheds in northern and central Taiwan based on Geographic Information Systems technique to establish an impact factor database for groundwater fluctuation analysis. From the analysis, the parameters which affect the groundwater level can be summarized as the accumulate rainfall, rainfall strength, length of main stream and average watershed elevation. Also, through the Back-Propagation Neural Network (BPNN) analysis, factors which affect the groundwater level are analyzed and thus the most significant Neural network parameter can then be determined. This study uses the transfer function in a single water basin through Neural Network Propagation method to find out the interactive relationship between the groundwater level and the rainfall event. Moreover, this study reveals that similar prediction is also quite effective for Li-San watershed. Through the entire analysis, the errors of simulation for three of the selected watersheds are low enough although the groundwater levels do not show much relevance with the physiographic factors which are chosen. The results also show that the chosen impact factors are somewhat well representative and the method herewith is effective in the simulation of the groundwater level fluctuation.