應用專家系統於地下水模式參數檢定之研究─以濁水溪沖積扇為例

Automatic Parameters Identification of Groundwater Model using Expert System - Case Study of Choshuihsi Alluvial Fan

王雲直(Yun-Chih Wang)

指導教授 ： 張良正

國立交通大學/工學院/土木工程系所/碩士(2010年)

https://doi.org/10.6842/NCTU.2010.00124

摘要

傳統上地下水參數檢定可分為人工參數檢定與自動化參數檢定；人工參數檢定之檢定過程需要人為方式決定參數值，其優點為所得之參數值較可解釋且過程較彈性，惟缺點為費時且需有豐富的模擬經驗始能進行。自動化參數檢定通常是結合地下水流模式與優選法，雖然可免去人工參數檢定的繁瑣而有較高效率，但優選法須先建立嚴謹的優選模式，並且將構想與經驗轉化為優選模式之目標函數與限制式，其轉換過程較複雜而抽象，因而限制了其可應用的問題型態，且當模式維度提高時，計算複雜度將大為提高。
有鑑於此，本研究整合地下水流模式與專家系統，發展自動化地下水參數檢定系統，其能兼顧人工檢定的可解釋性與彈性及自動化參數檢定的效率。此外為驗證系統之實用性，乃選定濁水溪沖積扇進行模式參數檢定之實例應用，檢定之參數為穩態情形下的淨補注量或抽水量(Q)。結果顯示，濁水溪沖積扇之淨補注量為每年12.41億噸，淨抽水量為每年12.75億噸，比較各種相關研究文獻，顯示此淨抽水量值應屬合理範圍。另外，分析檢定後抽水量之空間分布，發現與土地利用所反映的可能用水狀況趨勢一致。經再深入分析淨抽水量檢定過程發現，上、中游地區為模式主要補注區，對整體地下水位影響較大，因此檢定初期主要在調整此兩地區之參數值，接著下游區域才有較大幅變動，另外因上、中游易互相影響，所以需較多迭代次數，始能調整至合理值。下游地區因影響區域較小，且表層鄰近定水頭邊界，下層阻水層較完整，所以可較快調整至合理值。
本研究經過實際案例之驗證後，證實本研究所開發之自動化地下水參數檢定系統為一實際可行之參數檢定方法，且本系統因為使用規則式專家系統為參數檢定之核心，因此僅需將有關地下水參數檢定相關之經驗或想法，歸納成規則，即可應用於參數檢定上，而歸納之規則乃儲存於知識庫，可方便增加累積，因此系統檢定參數的能力易於擴充。

關鍵字

地下水 ； 檢定 ； 專家系統 ； 濁水溪 ； 補注 ； 抽水

並列摘要

Conventional parameter identification for groundwater models can be classified into manual parameter identification and automatic parameter identification. Manual parameter identification requires manual decisions to define parameter values. The resulting parameter values can be interpreted, and this process is flexible. However, this method is time consuming and requires expert analysis. Conversely, automatic parameter identification is based on the optimization method, and is computationally efficient. This method represents concepts and experiences as objective functions and constraints. This correlation is complicated and abstract; the application of this method is often limited to complicated field problems. The computational loading of the optimization method increases significantly when the parameters dimension is large.
Based on previous discussion, this study integrates a rule-based expert system and a groundwater simulation model, MODFLOW 2000, to develop an automatic groundwater parameter identification system. The proposed model has the manual identification advantages of interpretability and flexibility as and the automatic identification advantage of efficiency. To demonstrate the capability of solving a large field problem, this study proposes a model to identify the parameters for a simulation of the Choshuihsi Alluvial Fan. This study develops a steady state simulation model and estimates steady recharge rates. Results indicate that the total recharge of the Choshuihsi Alluvial Fan from rain and rivers is 1.241 billion metric tons, and its total pumping rate is 1.275 billion metric tons. These results are comparable to previous studies. Moreover, the spatial distribution of the pumping rate is consistent with the potential water use, or land use. An in-depth analysis shows that the upstream of the fan is the main recharge area, and affects the groundwater of whole alluvial fan. Hence, in the initial stages of identification, the system mainly modifies the parameters in the upstream area. Due to the interaction between upstream and midstream areas, the model required more iterations to obtain reasonable values for those areas. The downstream, coastal area has a Dirichlet boundary in the surface layer. The downstream aquiclude has greater coverage in lower layers, making it easier for the parameters to converge.
This field case study demonstrates the feasibility and capability of the proposed model. The expert system is the kernel to modify the parameters. Therefore, the model can increase its parameter identification capacity by adding new rules to the system.

並列關鍵字

groundwater ； identification ； expert system ； Choshuihsi ； recharge ； pumping rate

參考文獻

29.陳韋圻，應用專家系統於地下水模式自動化參數檢定之研究，國立交通大學，碩士論文，民國97年。

1.K.W. Chau, “Intelligent manipulation of calibration parameters in numerical modeling”, Advances in Environmental Research, 8, p. 467–476, 2004.

2.K.W. Chau, “Selection and calibration of numerical modeling in flow and water quality”, Environmental Modeling and Assessment, 9, p. 169–178, 2004.

3.K.W. Chau, “A review on integration of artificial intelligence into water quality modeling”, Marine Pollution Bulletin, 52, p. 726–733, 2006.

4.K.W. Chau, “A review on the integration of artificial intelligence into coastal modeling”, Journal of Environmental Management, 80, p. 47–57, 2006.