River Flow Forecasting using Recurrent Neural Networks

D. Nagesh Kumar (), K. Srinivasa Raju and T. Sathish

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2004, vol. 18, issue 2, 143-161

Abstract: Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to forecast monthly river flows. Two different networks, namely the feed forward network and the recurrent neural network, have been chosen. The feed forward network is trained using the conventional back propagation algorithm with many improvements and the recurrent neural network is trained using the method of ordered partial derivatives. The selection of architecture and the training procedure for both the networks are presented. The selected ANN models were used to train and forecast the monthly flows of a river in India, with a catchment area of 5189 km 2 up to the gauging site. The trained networks are used for both single step ahead and multiple step ahead forecasting. A comparative study of both networks indicates that the recurrent neural networks performed better than the feed forward networks. In addition, the size of the architecture and the training time required were less for the recurrent neural networks. The recurrent neural network gave better results for both single step ahead and multiple step ahead forecasting. Hence recurrent neural networks are recommended as a tool for river flow forecasting. Copyright Kluwer Academic Publishers 2004

Keywords: forecasting; hydrologic time series; recurrent neural networks; river flows (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (28)

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DOI: 10.1023/B:WARM.0000024727.94701.12

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