Notice of RetractionComparative study of Jorden and Elman model of neural network for short term flood forecasting

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates a temporal approach by applying Jordan and Elman network for rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using recurrent connections. Methodologies and techniques of the two models are presented in this paper and a comparison of the short term runoff prediction results between them is also conducted. The prediction results of the Jordan network indicate a satisfactory performance in the three hours ahead of time prediction. The conclusions also indicate that the Jordan network is more versatile than Elman model and can be considered as an alternate and practical tool for predicting short term flood flow.