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研究生:曾姵茵
研究生(外文):Pei-Yin Zeng
論文名稱:基因演算法對格網式分佈型降雨逕流模式建置之應用與比較
指導教授:游保杉游保杉引用關係溫清光溫清光引用關係
指導教授(外文):Pao-Shan YuChin-Gung Wen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:環境工程學系碩博士班
學門:工程學門
學類:環境工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:128
中文關鍵詞:分佈型降雨逕流模式基因演算法水質模式檳榔園
外文關鍵詞:Genetic algorithmwater quality modelDistributed rainfall-runoff modelArecanut garden.
相關次數:
  • 被引用被引用:4
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摘要

  本研究主要是以Fortran編寫基因演算法後與分佈型降雨逕流模式互相結合,自動率定出模式中所需要之參數並找出基因演算法的最佳設定;sGA最佳基本設定:族群數=100、字串長度=16位元、以均勻交換進行基因交換、基因交換機率=0.6、突變機率=0.02,而μGA最佳基本設定:族群數=5、字串長度=16位元、以均勻交換進行基因交換且基因交換機率=1。

  以μGA進行分佈型降雨逕流模式之參數率定後,把得到的Ch參數與實際的降雨強度、臨前條件做回歸,可得判斷係數0.7以上的高度相關,符合了Ch參數之物理意義;過去常以SCE法進行自動率定,與μGA相比較,在小集水區SCE法的率定時間為μGA法之3.5倍,大集水區為1.5倍,而搜尋到的最佳參數兩者方法則沒有明顯的不同。

  以μGA法結合分佈型降雨逕流模式後,推算大埔檳榔園全年地表逕量,再配合土壤沖刷模式、溶解性營養鹽模式及沉積性營養鹽等水質模式來模擬降雨期間地表逕流各污染物的產生量,並且以實際測定值來率定模式中所需要之參數。然後可求得大埔檳榔園地表逕流污染物年輸出量(單位:kg/ha-yr):SS=1650、NO3—N=0.025、TN=9.59、PO4—P=1.55、TP=2.5。
Abstrate

  This study aims to develop a FORTRAN-coded Genetic Algorithm (GA) combined with a distributed rainfall-runoff (DRR) model for calibrating the model parameters and finding the optimal settings of the GA. In the results of the sGA, the optimal settings have been found as the population number= 100, the string length= 16 bits, the probability of crossover=0.6, and the probability of mutation= 0.02. However the results of μGA show the population number= 5, the string length= 16 bits, the probability of crossover=1, and the probability of mutation= 1.

  The μGA also shows the calibration of DRR model and finds the Ch parameter has high correlation (R2=0.7) with the precipitation density and pre-rainfall condition. The result well fits the physical phenomenon of the Ch parameter. With comparison of SCE method, the consumption time would be reduced 3.5 times in small watersheds and 1.5 times in large watersheds. However, there is no difference in Ch value between SCE and μGA.

  After well-developed μGA-DTRR model, this study has calculated the annual pollution loads of the arecanut garden in the Da-Pu area by ways of the soil-erosion model, solute nutrient model, and settlementation nutrient model.The results present SS=1650 kg/ha-yr, NO3—N=0.025 kg/ha-yr, TN=9.59 kg/ha-yr, PO4—P=1.55 kg/ha-yr, and TP=2.5 kg/ha-yr.
第一章 前言
1.1 研究動機 1-1
1.2 論文內容與流程 1-2
第二章 文獻回顧
2.1 降雨逕流模式 2-1
2.2 模式參數最佳化方法 2-3
2.2.1 局部傳統式最佳化法 2-3
2.2.2 全域演化式最佳化法 2-3
2.2.3 基因演算法 2-7
2.2.3-1 簡單基因演算 2-8
2.2.3-2 微基因演算 2-9
2.2.4 自動率定法 2-11
第三章 基因演算法推求與分佈型降雨逕流模式最佳參數之應用
3.1 分佈型降雨逕流模式 3-1
3.2 基因演算原理 3-6
3.3 基因演算方法與流程 3-7
3.3.1 編碼與解碼 3-9
3.3.2 適存值 3-9
3.3.3 基因交換 3-15
3.3.4 突變 3-17
3.3.5 排列與選擇 3-18
3.3.6 菁英策略 3-20
3.3.7 演化 3-20
3.4 微基因演算法
3.4.1 基因交換 3-22
3.4.2 突變 3-22
3.4.3 選擇 3-22
3.4.4 演化 3-22
第四章 應用實例
4.1 小集水區-大埔檳榔園 4-1
4.1.1 水文模式 4-2
4.1.2 流量雨量測定方法 4-4
4.1.3 降雨事件 4-4
4.1.4 建置最佳化模式 4-4
4.1.5 討論 4-17
4.2 非點源水質模式
4.2.1 水質模式 4-18
4.2.2 建置最佳化模式 4-20
4.2.3 檳榔園全年污染量估計與討論 4-25
4.3 大集水區-鹽水溪流域
4.3.1 流域概況 4-28
4.3.2 流域內水文站與水庫概況 4-28
4.3.3 基本資料 4-30
4.3.4 降雨事件 4-31
4.3.5 建置最佳化模式 4-31
4.3.6 討論 4-36
第五章 結果與討論
5.1 基因演算法參數之決定
5.1.1 簡單基因演算法 5-1
5.1.2 微基因演算法 5-7
5.1.3 基因演算法參數最佳設定 5-7
5.2 分佈型降雨逕流模式自動率定法
5.2.1 最佳參數比較 5-8
5.2.2 參數穩定度與合理性 5-10
5.2.3 最佳化結果比較 5-11
5.2.4 搜尋速度 5-12
5.3 非點源污染負荷推估 5-13
第六章 結論與建議
6.1 結論 6-1
6.2 建議 6-1

參考文獻
附錄一
附錄二
附錄三
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