Abstract—The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates a temporal approach by applying Time lagged recurrent neural network and general recurrent 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 general 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 Time lagged recurrent neural network indicate a satisfactory performance in the three hours ahead of time prediction. The conclusions also indicate that Time lagged recurrent neural network is more versatile than general recurrent neural network and can be considered as an alternate and practical tool for predicting short term flood flow.
Index Terms—Artificial neural network, Forecasting, Rainfall, Runoff, Models, Prediction, General recurrent neural network, Time lagged recurrent neural network.
Rahul P. Deshmukh, Indian Institute of Technology, Bombay Powai, Mumbai, India (email: firstname.lastname@example.org).
A. A. Ghatol, Former Vice-Chancellor, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, India (email:email@example.com).
Cite: Rahul P. Deshmukh and A. A. Ghatol, "Short Term Flood Forecasting Using Recurrent Neural Networks a Comparative Study," International Journal of Engineering and Technology vol. 2, no. 5, pp. 430-434, 2010.