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.

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