Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A novel approach to robust parameter estimation using neurofuzzy systems
Mathematics and Computers in Simulation
Advances in Engineering Software
An adaptive neuro-fuzzy inference system for bridge risk assessment
Expert Systems with Applications: An International Journal
Constructing neural network sediment estimation models using a data-driven algorithm
Mathematics and Computers in Simulation
Advances in Engineering Software
Applying artificial neural networks in environmental prediction systems
ICAI'10 Proceedings of the 11th WSEAS international conference on Automation & information
The neural network-based forecasting in environmental systems
WSEAS Transactions on Systems and Control
Hydraulic head interpolation using anfis-model selection and sensitivity analysis
Computers & Geosciences
A fuzzy-autoregressive model of daily river flows
Computers & Geosciences
Application of adaptive network based fuzzy inference system method in economic welfare
Knowledge-Based Systems
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Accurate estimation of River flow changes is a quite important problem for a wise and sustainable use. Such a problem is crucial to the works and decisions related to the water resources and management. In this study, an adaptive network-based fuzzy inference system (ANFIS) approach was used to construct a River flow forecasting system. In particular, the applicability of ANFIS as an estimation model for River flow was investigated. To illustrate the applicability and capability of the ANFIS, the River Great Menderes, located the west of Turkey and the most important water resource of Great Menderes Catchment's, was chosen as a case study area. The advantage of this method is that it uses the input-output data sets. Totally 5844 daily data sets collected in 1985-2000 years were used to estimate the River flow. The models having various input structures were constructed and the best structure was investigated. In addition four various training/testing data sets were constructed by cross validation methods and the best data set was investigated. The performance of the ANFIS models in training and testing sets were compared with the observations and also evaluated. The results indicated that the ANFIS can be applied successfully and provide high accuracy and reliability for River flow estimation.