ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

River Flow Forecasting using Recurrent Neural Networks

Kumar, Nagesh D and Srinivasa Raju, K and Sathish, T (2004) River Flow Forecasting using Recurrent Neural Networks. In: Water Resources Management, 18 (2). pp. 143-161.

[img] PDF
River_Flow.pdf - Published Version
Restricted to Registered users only

Download (275kB) | Request a copy
Official URL: http://www.springerlink.com/content/x19l5128641766...


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 ddition, 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

Item Type: Journal Article
Additional Information: Copyright of this article belongs to Kluwer Academic Publishers.
Keywords: forecasting;hydrologic time series;recurrent neural networks; river flows
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Depositing User: Obaiah B
Date Deposited: 03 Jul 2007
Last Modified: 20 Jan 2012 05:19
URI: http://eprints.iisc.ac.in/id/eprint/11380

Actions (login required)

View Item View Item