%0 Journal Article %1 IJACSA.2010.010514 %A Rahul P Deshmukh, A A Ghatol %D 2010 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K Forecasting, Models., Rainfall, Runoff,Artificial network neural %N 5 %T Modular neural network approach for short term flood forecasting a comparative study %U http://ijacsa.thesai.org/ %V 1 %X The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Modular feedforward neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modular neural network modeling. Methodologies and techniques for four 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 Modular feedforward neural network with model two indicate a satisfactory performance in the three hours ahead of time prediction. The conclusions also indicate that Modular feedforward neural network with model two is more versatile than other and can be considered as an alternate and practical tool for predicting short term flood flow.

@article{IJACSA.2010.010514, abstract = {The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Modular feedforward neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modular neural network modeling. Methodologies and techniques for four 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 Modular feedforward neural network with model two indicate a satisfactory performance in the three hours ahead of time prediction. The conclusions also indicate that Modular feedforward neural network with model two is more versatile than other and can be considered as an alternate and practical tool for predicting short term flood flow. }, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Rahul P Deshmukh}, A A Ghatol}, biburl = {https://www.bibsonomy.org/bibtex/2c7d208d6dbc26e5a1676e0af078c7623/thesaiorg}, interhash = {61bfae18f5bc5dd8baa874c06b9fe344}, intrahash = {c7d208d6dbc26e5a1676e0af078c7623}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {Forecasting, Models., Rainfall, Runoff,Artificial network neural}, number = 5, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Modular neural network approach for short term flood forecasting a comparative study}}, url = {http://ijacsa.thesai.org/}, volume = 1, year = 2010 }