Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system
Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system
复制标题
DOI：
10.1016/j.jhydrol.2007.10.050
复制
发表时间：
2008-01
影响因子：
6.4
通讯作者：
C. Shu;T. Ouarda
中科院分区：
文献类型：
--
作者：
C. Shu;T. Ouarda
本文提出了一种利用自适应神经模糊推理系统（ANFIS）进行无资料站点洪水分位数估计的方法。该方法具有模糊模型的系统辨识和解释能力以及人工神经网络的学习能力。使用减法聚类算法的ANFIS的结构进行识别。系统训练采用反向传播和最小二乘估计相结合的混合学习算法。ANFIS方法提供了一种综合机制，用于识别水文区域，从数据中生成知识，提供洪水估计和自我调整，以实现最佳性能。所提出的方法被应用到151集水区在加拿大的魁北克省，并进行了比较，人工神经网络的方法，非线性回归（NLR）的方法和非线性回归与区域化的方法（NLR-R）。一个刀切过程是用于三种方法的性能评价。结果表明，ANFIS方法具有更好的泛化能力比NLR和NLR-R的方法，并与人工神经网络的方法。
In this paper, the methodology of using adaptive neuro-fuzzy inference systems (ANFIS) for flood quantile estimation at ungauged sites is presented. The proposed approach has the system identification and interpretability of fuzzy models and the learning capability of artificial neural networks (ANNs). The structure of the ANFIS is identified using the subtractive clustering algorithm. A hybrid learning algorithm consisting of back-propagation and least-squares estimation is used for system training. The ANFIS approach provides an integrated mechanism for identifying the hydrological regions, generating knowledge from the data, providing flood estimates and self-tuning to achieve the optimal performance. The proposed approach is applied to 151 catchments in the province of Quebec, Canada, and is compared to the ANN approach, the nonlinear regression (NLR) approach and the nonlinear regression with regionalization approach (NLR-R). A jackknife procedure is used for the evaluation of the performances of the three approaches. Results indicate that the ANFIS approach has a much better generalization capability than the NLR and NLR-R approaches and is comparable to the ANN approach.