應用模糊理論分析降雨空間變異性在非點源污染模擬

Applying Fuzzy Theory to Analyze Rainfall Variation for Simulating Non-Point Source Pollution

為減少此誤差且讓流量及非點源污染量推估與實際情況更相符，本研究由傳統反距離指數法的概念出發，發展一套模糊化修正式反距離指數法，應用模糊理論(Fuzzy theory)中隸屬度的概念，分析降雨在集水區中之空間變異性。並採用WinVAST模式進行流量及非點源污染量之模擬與推估，將整個集水區依據地形再劃分為數個子流域，以各子流域區塊為各別輸入的單位，透過更詳盡地輸入資料，且運用模糊化的屬性特性，解決傳統降雨資料分析之限制，以期更完整地描述各子流域區塊之實際降雨特性，進而提高流量及非點源污染量推估之準確性。由結果可知，當降雨不具有空間變異性時，無論採用何種降雨推估方式，無論周圍雨量站權重如何分配，所推估出之降雨均不受影響，當然，所模擬出之流量及非點源污染量亦相同。隨著空間高程呈正向關係之暴雨、降雨地區性集中之暴雨，採用模糊化修正式反距離指數法，均能有效地降低降雨推估誤差，及提高流量與非點源污染模擬結果之準確性，尤其是與高程具有絕對相關之設計暴雨，此方法之改善更為明顯。而隨機空間變異之降雨，採用模糊化修正式反距離指數法，對於降雨推估及流量與非點源污染模擬之準確性，並無明顯提升的效果。

Rainfall is the most important input for watershed modeling. Precipitation characteristics, such as rainfall intensity and duration, usually exhibit significant spatial variation, even within small watersheds. Therefore, properly describing the spatial variation of rainfall is essential for predicting the water movement in a watershed. Varied circumstances require a variety of suitable methods for interpolating and estimating precipitation. In this study, a modified method, combining the inverse distance method and fuzzy theory, was applied to precipitation interpolation. Meanwhile, genetic algorithm (GA) was used to determine the parameters of fuzzy membership functions, which represent the relationship between the location without rainfall records and its surrounding rainfall gauges. The objective in the optimization process was to minimize the estimated error of precipitation. The results show that the estimated error is usually reduced by this method. Particularly, when there are large and irregular elevation differences between the interpolated area and its vicinal rainfall gauging stations, it is important to consider the effect of elevation differences, in addition to the effect of horizontal distances. Reliable modeling results can substantially lower the cost for the watershed management strategy. The modeling results with spatial differences characteristics can also be used for deciding the sequence of precedence management in a watershed, and developing the optimal allocation of Best management practices (BMPs).