Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2759

Full Length Research Paper

Neuro-fuzzy modelling and forecasting in water resources

Hadi Galavi1* and Lee Teang Shui2
1Department of Civil, Faculty of Engineering, University Putra Malaysia (UPM), Malaysia. 2Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Malaysia.
Email: [email protected]

  •  Accepted: 28 May 2012
  •  Published: 28 June 2012


Recently, with rapid development in computer science and technology, artificial intelligent (AI) models that emulate human thinking ability and brain structure are increasingly used in hydrological forecasting context. Neuro-fuzzy (N-F) models or specifically adaptive neuro-fuzzy inference systems (ANFIS) are rapidly becoming conventional in either academic or industrial applications. Although, there is a common network structure among ANFIS models, there is no one-fits-all ANFIS architecture for every case. Moreover, it is discussed that in many application, theory does not guide in model building process by either suggesting the relevant model input variables or correct functional form and model configuration. This paper is focused on the application of ANFIS in water resources context and reviews the common architecture of ANFIS models been used in this area of research. The aim is to familiarize the new researchers with ANFIS application process in water resources studies.


Key words: Neuro-fuzzy modelling, adaptive neuro-fuzzy inference systems (ANFIS), hybrid learning algorithm, subtractive clustering.