A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Patel, Shivshanker Singh and Ramachandran, Parthasarathy (2015) A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin. In: WATER RESOURCES MANAGEMENT, 29 (2, SI). pp. 589-602.

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Abstract

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Item Type:	Journal Article
Publication:	WATER RESOURCES MANAGEMENT
Publisher:	SPRINGER
Additional Information:	Copyright for this article belongs to the SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
Keywords:	River water discharge modeling; ARIMA; Artificial neural networks; Support vector regression
Department/Centre:	Division of Interdisciplinary Sciences > Management Studies
Date Deposited:	14 Feb 2015 13:34
Last Modified:	14 Feb 2015 13:34
URI:	http://eprints.iisc.ac.in/id/eprint/50813

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