Using adaptive neuro-fuzzy inference system for hydrological time series prediction

Authors:
Mohammad Zounemat-Kermani;Mohammad Teshnehlab
Affiliations:
K.N. Toosi University of Technology, Department of Civil Engineering, Tehran, Iran;K.N. Toosi University of Technology, Department of Electronic Engineering. Tehran, Iran
Venue:
Applied Soft Computing
Year:
2008

Citing 6
Cited 10

Prediction of extreme precipitation using a neural network: application to summer flood occurence in Moravia

Advances in Engineering Software
Data Engineering: Fuzzy Mathematics in Systems Theory and Data Analysis

Data Engineering: Fuzzy Mathematics in Systems Theory and Data Analysis
Generalized regression neural network in modelling river sediment yield

Advances in Engineering Software
Neural network and neuro-fuzzy assessments for scour depth around bridge piers

Engineering Applications of Artificial Intelligence
Suitability of different neural networks in daily flow forecasting

Applied Soft Computing
Rainfall-runoff model usingan artificial neural network approach

Mathematical and Computer Modelling: An International Journal

Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system

Applied Soft Computing
Non-uniform attractor embedding for time series forecasting by fuzzy inference systems

Neurocomputing
Evolutionary algorithms for the selection of time lags for time series forecasting by fuzzy inference systems

Neurocomputing
Research on forecasting method of urban water demand based on fuzzy theory

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
Analytical inference model for prediction and customization of inter-agent dependency requirements

ACM SIGSOFT Software Engineering Notes
A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting

Engineering Applications of Artificial Intelligence
Knowledge discovery by an intelligent approach using complex fuzzy sets

ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Self-learning complex neuro-fuzzy system with complex fuzzy sets and its application to adaptive image noise canceling

Neurocomputing
Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff

Applied Soft Computing
Adaptive image restoration by a novel neuro-fuzzy approach using complex fuzzy sets

International Journal of Intelligent Information and Database Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Conventionally, the multiple linear regression procedure has been known as the most popular models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, intelligence system approaches such as artificial neural network (ANN) and neuro-fuzzy methods have been used successfully for time series modelling. In most instances for neural networks, multi layer perceptrons (MLPs) that are trained with the back-propagation algorithm have been used. The major shortcoming of this approach is that the knowledge contained in the trained networks is difficult to interpret. Using neuro-fuzzy approaches, which enable the information that is stored in trained networks to be expressed in the form of a fuzzy rule base, would help to overcome this issue. In the present study, a time series neuro-fuzzy model is proposed that is capable of exploiting the strengths of traditional time series approaches. The aim of this article is to investigate the potential of a neuro-fuzzy system with a Sugeno inference engine, considering different numbers of membership functions. Three rivers have been selected and daily prediction for them was applied. For better judgment, outcomes of the network have been compared to an autoregressive model.