作者
Vahid Nourani, Özgür Kisi, Mehdi Komasi
发表日期
2011/5/13
期刊
Journal of Hydrology
卷号
402
期号
1-2
页码范围
41-59
出版商
Elsevier
简介
The need for accurate modeling of the rainfall–runoff process has grown rapidly in the past decades. However, considering the high stochastic property of the process, many models are still being developed in order to define such a complex phenomenon. Recently, Artificial Intelligence (AI) techniques such as the Artificial Neural Network (ANN) and the Adaptive Neural-Fuzzy Inference System (ANFIS) have been extensively used by hydrologists for rainfall–runoff modeling as well as for other fields of hydrology. In this paper, two hybrid AI-based models which are reliable in capturing the periodicity features of the process are introduced for watershed rainfall–runoff modeling. In the first model, the SARIMAX (Seasonal Auto Regressive Integrated Moving Average with exogenous input)-ANN model, an ANN is used to find the non-linear relationship among the residuals of the fitted linear SARIMAX model. In the …
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