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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105876

    Title: 以自組特徵映射與非線性自回歸網路於區域地下水之預測
    Other Titles: Self-organizing map and nonlinear autoregression networks for regional groundwater forecasting
    Authors: 黃冠文;Huang, Kuan-Wen
    Contributors: 淡江大學水資源及環境工程學系碩士班
    張麗秋;Chang, Li-Chiu
    Keywords: 類神經網路;自組特徵映射網路;倒傳遞類神經網路;拓樸特徵;區域相對水位預測;artificial neural networks;Nonlinear autoregressive with exogenous inputs(NARX);Self-organizing map (SOM);Topological characteristics;Regional relatively groundwater level forecasting model
    Date: 2015
    Issue Date: 2016-01-22 15:07:31 (UTC+8)
    Abstract: 世界氣候日趨極端,水資源匱乏係目前全球共同面臨的問題,臺灣受限於地形及降雨時空不均,台灣每人分配到的雨量是世界的中低標,而地下水因成本低廉、抽取便利,常在地表水供應不足的情況下,成為重要的替代水源之一,如何有效的保育及補注地下水資源已成為重要的議題。濁水溪沖積扇地下水區為良好的天然補注區,如能掌握與預測地下水位變化趨勢,有助於地表水與地下水的聯合運用與調配管理之決策參考。
    World climate becoming more extreme, department of water scarcity problem currently facing the world''s, Taiwan is limited by time and space uneven terrain and rainfall, each person assigned to the low rainfall is the world standard. How to preserve and recharge groundwater effectively has become an important issue.Groundwater has become an important water resource because of its low cost and easy extraction, often in the absence of sufficient surface water supply, it has become an important alternative water sources. The alluvial of the Zhuoshui River are good natural recharge areas of groundwater. Change of control and forecasting of groundwater, assist decision-making joint use and allocation management of surface water and groundwater reference.
    In this study, the study area is in the upstream mountain, upstream proximal-fan, midstream proximal-fan and downstream proximal-fan of Zhuoshui River. Collect the daily long-term (2000-2013) regional data sets and pre-processthe data of surface water and groundwater. Discussion groundwater aquifers of different districts and distribution and change, in non-hierarchical non-district "region mode" and "hierarchical partitioning mode" stratified-district of study area. The process is divided into build mode: data processing, SOM classification analysis, NARX.
    The results show that groundwater in the study area of classification, in 5X5 network is the most appropriate size. Available representative groundwater table, the amount of the spatial distribution of topography, and effective analysis of each neuron characteristics in agricultural water (irrigation and aquaculture water) at different times. NARX average groundwater level forecast model for the region quite excellent performance, R^2 are over 0.99 above. SOM-NARX mode groundwater variation prediction mode hierarchical partitioning of the region, in a hierarchical partitioning scheme outperformed the region''s performance mode, north of layering and zoning pattern by pattern outperformed the south coast of Zhuoshui River.
    Appears in Collections:[水資源及環境工程學系暨研究所] 學位論文

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