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Rainfall-runoff modelling using a wavelet-based hybrid SVM scheme

Remesan, R., Bray, Michaela, Shamim, M. A. and Han, D. 2009. Rainfall-runoff modelling using a wavelet-based hybrid SVM scheme. Presented at: Hydroinformatics in hydrology, hydrogeology and water resources, Hyderabad, India, 6-12 September 2009. Published in: Cluckie, I. D., Chen, Y., Babovic, V., Konikow, L. F., Mynett, A., Demuth, S. and Savic, D. eds. Hydroinformatics in Hydrology, Hydrogeology and Water Resources - Proceedings of Symposium JS.4 at the Joint Convention of the IAHS & IAH; Hyderabad 6-12 September 2009. IAHS Proceedings & Reports , vol. 331. Hyderabad: IAHS Press, pp. 41-50.

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Abstract

Efficient flood forecasting based on rainfall-runoff modelling is an important non-structural approach for flood mitigation. The support vector machine, a novel artificial intelligence-based method developed from statistical learning theory, is adopted herein in conjunction with wavelets to establish a realtime flood forecasting model. We compared them with another hybrid model called the neuro-wavelet model (NW). The methods were tested using the data from a small watershed (the Brue catchment in southwest England UK), for which 7 years of records were available. The results reveal that the wavelet-based hybrid models can provide accurate runoff estimates for flood forecasting in the Brue catchment. In this study the training data length and input data structure were determined using another novel technique, the gamma test. Copyright © 2009 IAHS Press.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TD Environmental technology. Sanitary engineering
Uncontrolled Keywords: Flood; Gamma test; Hybrid models; Model selection; Neural networks; Support vector machine
Additional Information: Proceedings of Symposium JS.4 at the Joint Convention of the International Association of Hydrological Sciences (IAHS) and the International Association of Hydrogeologists (IAH)
Publisher: IAHS Press
ISBN: 9781907161025
Related URLs:
Last Modified: 04 Jun 2017 03:08
URI: http://orca.cf.ac.uk/id/eprint/17295

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