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    題名: 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
    其他題名: Self-organizing map and nonlinear autoregression networks for regional groundwater forecasting
    作者: 黃冠文;Huang, Kuan-Wen
    貢獻者: 淡江大學水資源及環境工程學系碩士班
    張麗秋;Chang, Li-Chiu
    關鍵詞: 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
    日期: 2015
    上傳時間: 2016-01-22 15:07:31 (UTC+8)
    摘要: 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。
    本研究之研究區域為濁水溪流域的上游山區與下游扇頂、扇央及扇尾之沖積扇區,資料以研究區域內主要河川流量站、地下水觀測井與雨量站等2000-2013年日觀測資料。為探討濁水溪沖積扇不同分區與含水層之地下水分布與變化,將研究區域以不分層不分區之「全區模式」與分層分區之「分層分區模式」,分別建置SOM─NARX區域地下水位變化預測模式。建置模式之流程分為:資料處理、SOM分類分析、NARX。
      結果顯示濁水溪沖積扇之日地下水變化量分類,以5X5之SOM網路大小最為合適,可得具有代表區域地下水位變化量空間分布之拓樸圖,並透過資料統計的方式,有效分析各神經元於農業用水(灌溉用水及養殖用水)不同時期之特徵。NARX模式對於區域平均地下水位預測成效相當優異,R^2皆超過0.99以上。SOM-NARX模式在全區與分層分區之濁水溪區域地下水變化量預測模式,以分層分區模式表現優於全區模式表現、分層分區以北按模式表現優於南岸模式。
    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.
    顯示於類別:[水資源及環境工程學系暨研究所] 學位論文

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