Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/5191
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dc.contributor.advisor林明德zh_TW
dc.contributor.advisorC.J.LUen_US
dc.contributor.author林妙貞zh_TW
dc.contributor.authorLIN, Maio-chenen_US
dc.date1997zh_TW
dc.date.accessioned2014-06-06T06:34:14Z-
dc.date.available2014-06-06T06:34:14Z-
dc.identifier.urihttp://hdl.handle.net/11455/5191-
dc.description.abstractThe most difficult job of applying the groundwater management models to the contaminated aquifer remediation problems is the consideration of the influence of the uncertainties according to the unknown aquifer properties and parameters. In the past, the parameters of the aquifer flow and contamination transport models are often assumed as known already. However, in the real aquifer conditions, the uncertainties of the modeling parameters play a very important role and will significantly affect the reliability of the modeling results. This research develops an optimization model which combines genetic algorithms with a groundwater simulation model to solve groundwater remediation problems. In light of the importance of the parameter uncertainty, the spacious uncertainty of the conductivity of the aquifer is also taken into account for modeling analysis to investigate its impact upon the reliability of the groundwater remediation schemes. The performances of the genetic operators and the parameters used in the genetic algorithms model are also evaluated in this research to find out the suitable operators and parameter values for the case studied in this research. Furthermore, a new penalty function is used to improve the handling of the constraint violation. The objectives of the pump-and- treat remediation systems are to minimize the total pumping rates and the total number of pumping wells. There are two different situations:(1) only pumping wells are used;(2) both pumping and injection wells are considered. The optimal remediation strategies are obtained from the optimization model by considering a single conductivity realization and muiltiple conductivity realizations. The reliability of each optimal remediation strategy is verified by Monte Carlo Simulation. The results show that the reliability increases as more conductivity relizations were taken into account in the optimization model. When 30 conductivity realizations were used, the reliability of the remediation strategy is about 90%. This shows that using multiple conductivity realizations as model constraints can actually get highly reliable remediation strategies. This research also finds that remediation strategies which use both pumping and injection wells have higher reliabilities and lower pumping rates, indicating that with complement of injection wells in the groundwater remediation systems, the cleanup goals could be reached more effectively. About the comparison of different types of objective functions, the results show that minimizing well numbers can more practically meet the requirement of minimizing cost.en_US
dc.language.isoen_USzh_TW
dc.publisher環境工程學系zh_TW
dc.subject遺傳演算法zh_TW
dc.title遺傳演算法在地下水復育系統的不確定性之應用zh_TW
dc.titleGenetic Algorithmsen_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.grantfulltextnone-
Appears in Collections:環境工程學系所
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