Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/5855
標題: 應用啟發式演算法於抽水源及污染源鑑定問題
Application of Heuristic Algorithms on groundwater pumping source and pollution source identification problems
作者: 王錦隆
Wang, Jin-Long
關鍵字: 遺傳演算法
genetic algorithm
粒子群優化
抽水源鑑定
污染源鑑定
particle swarm optimization
pumping source identification
pollution source identification
出版社: 環境工程學系所
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摘要: 近年來,人們對於水資源的需求與日俱增,地下水為台灣相當重要的水資源,如何保護地下水資源已成為刻不容緩的議題。地下水管理問題為高度複雜的非線性問題,傳統決策工具,如線性規劃等,常因易陷入局部最佳解導致無法求取實際最佳解;而啟發式演算法因擁有較佳的搜尋能力而可較容易求得全域最佳解。本研究使用遺傳演算法(Genetic Algorithm, GA)、粒子群優化(Particle Swarm Optimization, PSO)、兩者改良與混合模式、以及地下水模擬模式(MODFLOW及MT3DMS)求解地下水管理的優化問題,研究主軸為鑑定地下水之抽水源及污染源,藉由觀測井的水頭與濃度資料,推估抽水(污染)源位置、抽水(污染物釋放)時間及速率等,藉以協助鑑定違法抽井的位置,防止超抽地下水;亦可協助釐清污染場址的污染排放源,以正確地進行復育工作。研究結果顯示於抽水源鑑定問題均質案例中,各演算法均能成功求解,尤其混合模式成功率均達80%以上,求解能力相當穩定,而於非均質案例也呈現相同趨勢,其中以GAMUPSO求解效率最佳。此外,混合模式應用至污染源鑑定問題中成功率亦皆可達70%以上,求解能力仍相當不錯。
Groundwater is a major water resource in Taiwan; therefore, its protection becomes an important issue. This study investigates the identification of water extraction sites and pollution sources by estimating the location of the water extraction and pollution emission according to the hydraulic heads and concentrations of the observation wells. Since groundwater management problems are highly complex and non-linear that traditional optimization techniques could hardly find the global optimal solution. In contrast, heuristic algorithms provide better global search capability and can successfully solve complex optimization problems. This study employed genetic algorithm (GA), particle swarm optimization (PSO), their hybrid models, and groundwater simulation software (MODFLOW and MT3DMS) to solve the groundwater management problems afore mentioned. The results indicate that all optimization models developed here can successfully solve the groundwater management problems. Especially the hybrid models can all achieve success rates over 80% for solving homogeneous problems of identifying water extraction site, exhibiting stable optimization capabilities. Similar situations can be found in the heterogeneous cases. GAMUPSO especially exhibit the best optimization performance. Furthermore, hybrid models also achieve over 70% success rates for solving problems of identifying pollution emission site, still are proved as stable optimization techniques.
URI: http://hdl.handle.net/11455/5855
其他識別: U0005-1508201214231600
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1508201214231600
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