A rainfall-runoff model predicts surface runoff either using a physically-based approach or using a systems-based approach. Takagi-Sugeno (TS) Fuzzy models are systems-based approaches and a popular modeling choice for hydrologists in recent decades due to several advantages and improved accuracy in prediction over other existing models. In this paper, we propose a new rainfall-runoff model developed using Gustafson-Kessel (GK) clustering-based TS Fuzzy model. We present comparative performance measures of GK algorithms with two other clustering algorithms: (i) Fuzzy C-Means (FCM), and (ii)Subtractive Clustering (SC). Our proposed TS Fuzzy model predicts surface runoff using: (i) observed rainfall in a drainage basin and (ii) previously observed precipitation flow in the basin outlet. The proposed model is validated using the rainfall-runoff data collected from the sensors installed on the campus of the Indian Institute of Technology, Kharagpur. The optimal number of rules of the proposed model is obtained by different validation indices. A comparative study of four performance criteria: RootMean Square Error (RMSE), Coefficient of Efficiency (CE), Volumetric Error (VE), and Correlation Coefficient of Determination(R) have been quantitatively demonstrated for each clustering algorithm.
翻译:降雨径流模型预测地表径流。Takagi-Sugenno(TS) Fuzzy模型是基于系统的方法和近几十年来水文学家流行的模型选择,因为与其他现有模型相比,在预测方面有若干优势和准确性得到提高。在本文中,我们提出使用Gustafson-Kessel(GKK)基于集群的TS TS Fuzzy模型开发的新的降雨径流模型。我们用另外两种组合算法提出了GK算法的比较性能尺度:(一) Fuzzy C-Means(FCM)和(二) Actionive 集合算法(SC)。我们提议的TS Fuzzy模型预测了近几十年来水文学家的地表径流:(一) 在排水流域观测降雨量和(二) 先前观察到的河流域外降水流。拟议模型使用印度技术研究所校园安装的传感器Hharagpur(Kharagpur)收集的降水流数据。拟议模型的最佳规则数由不同的鉴定指数获得。我们提议的TSFornial Recal Requistrative Regal Regal Regal Regal Regislation(C) coal Regal Regal Regal Regal Regal Regal Regresslismlislisal)。