完整后设资料纪录
DC 栏位 | 值 | 语言 |
---|---|---|
dc.contributor.author | 傅怡钏 | en_US |
dc.contributor.author | 张良正 | en_US |
dc.date.accessioned | 2014-12-12T02:54:43Z | - |
dc.date.available | 2014-12-12T02:54:43Z | - |
dc.date.issued | 2006 | en_US |
dc.identifier.uri | http://140.113.39.130/cdrfb3/record/nctu/#GT009316546 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/78669 | - |
dc.description.abstract | 人工湖水源调配方式为台湾近年来新水源开发的重要考量之一,而高屏大湖为台湾第一个以大型平地人工湖为区域重要水源的案例,亦是至目前为止规划较为完整的案例,其本身由5个湖区组成,且因此区之地下水位高,在操作时地表水与地下水会有明显出/入渗交换机制,因此其操作营运方式较地表水库,或是仅具有补注作用的人工湖较为复杂,若能对高屏大湖之操作营运方式做进一步的探讨,不但可以增加其未来的效益,亦可做为其它地区人工湖开发的参考。 由于湖水与地下间之交换为高屏大湖系统重要的物理机制,且为非线性问题,因此,地下水反应不能以传统的处理方式,如响应矩阵法(Response Matrix Method)加以简化为线性反应,而必须以较复杂的地下水模式模拟之,惟若将此模式整合进以优选为导向的操作规划模式,则又将使整体的计算量增加太大。为此,在兼顾地下水系统模拟的精确度与计算效率的考量下,乃先以地下水数值模式MODFLOW 96与湖泊模组(LAK2)模拟产生湖水与地下水交互作用之相关资料,并以其训练并验证类神经网路,则此类神经网路可兼具高计算效率与描述非线性反应的能力,接着再将此类神经网路嵌入高屏大湖地表地下最佳操作规划模式中。模式中人工湖的操作乃引用一般地表水库常用之规线操作原则,并利用遗传演算法优选最佳操作规线。 本研究进一步以前述的最佳操作规划模式,探讨高屏大湖有无地下水系统的加入,与不同设计的操作规线,对系统供水效能之影响。研究结果显示,在未考量规线操作下,高屏大湖能与地下水交换之设计方案比高屏大湖为封底设计之方案,可增加48%的供应量且还能补注地下水; 进一步运用规线操作后,则能降低尖峰缺水量,而使SI降低21.77%;若增加规线弹性,SI最多可再降低44.6%。由本研究发现,地表地下交换之营运方式与规线操作皆对高屏大湖的供水能力皆有相当大的助益。 | zh_TW |
dc.description.abstract | Artificial lake is considered a new water resources alternative in Taiwan in recent years. Kao-ping Artificial Lake is an example and it is a lake system with five artificial ponds. The groundwater level surrounding the lake is higher than those in the surrounding areas. Therefore, the determination of available water from the system is more complex than those for reservoir operations. This research considers the effects of the water supplied by the lake, the water stored in the lake, and the exchange between the lake and groundwater system in the operations of the Kao-ping Artificial Lake. The research attempts to develop an optimal conjunctive model for the Kao-ping Artificial Lake. However, if a numerical model is used to simulate the behavior of Kao-ping Artificial Lake operations, it will increase the computational burden. In order to solve the problem, a Back Propagation Neural Network (BPN) trained by simulation results for MODFLOW 96 and LAK2 is applied to represent the nonlinear dynamic relationship between the lake and the unconfined aquifer. Secondly, the water to be provided by the system at each time step is determined by an optimal rule curve found by a Genetic Algorithm (GA). Results of this study indicate that the conjunctive use model can significantly increase the water supply reliability. In addition, the model also provides an optimal groundwater recharge strategy. The model is shown to be able to reduce the magnitude of the water shortage and increase the resilience of the operating rule curve and is therefore believed to be a promising tool in Kao-ping Artificial Lake’s future operations. | en_US |
dc.language.iso | zh_TW | en_US |
dc.subject | 操作规线 | zh_TW |
dc.subject | 类神经网路 | zh_TW |
dc.subject | 遗传演算法 | zh_TW |
dc.subject | 高屏大湖 | zh_TW |
dc.subject | operating rule | en_US |
dc.subject | Neural Networks | en_US |
dc.subject | Genetic Algorithm | en_US |
dc.subject | Kao-ping Artificial Lake | en_US |
dc.title | 多湖区系统最佳地表地下联合操作之研究 | zh_TW |
dc.title | Optimizing the Conjunctive Surface and Subsurface Operations of a Multi-Lake System | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | 土木工程学系 | zh_TW |
显示于类别: | Thesis |
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