Discharge Rating Curve Extension – A New Approach

Chandrasekaran Sivapragasam () and Nitin Muttil

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2005, vol. 19, issue 5, 505-520

Abstract: It is often necessary to have stage discharge curve extended (extrapolated) beyond the highest (and sometimes lowest) measured discharges, for river forecasting, flood control and water supply for agricultural/industrial uses. During the floods or high stages, the river may become inaccessible for discharge measurement. Rating curves are usually extended using log–log axes, which are reported to have a number of problems. This paper suggests the use of Support Vector Machine (SVM) in the extrapolation of rating curves, which works on the principle of linear regression on a higher dimensional feature space. SVM is applied to extend the rating curves developed at three gauging stations in Washington, namely Chehalis River at Dryad and Morse Creek at Four Seasons Ranch (for extension of high stages) and Bear Branch near Naselle (for extension of low stages). The results obtained are significantly better as compared with widely used logarithmic method and higher order polynomial fitting method. A comparison of SVM results with ANN (Artificial Neural Network) indicates that SVM is better suited for extrapolation. Copyright Springer Science + Business Media, Inc. 2005

Keywords: artificial neural network; forecasting; rating curve; regression; support vector machine (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11269-005-6811-2

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