Abstract
Traditional methods of streamflow analysis and synthesis are based upon information contained in individual data. These methods ignore information contained in and among groups of data. Recently, the concept of extracting information from data groupings through pattern recognition techniques has been found useful in hydrology. For streamflow analysis, this paper proposes several objective functions to minimize the classification error encountered in currently used techniques employing minimum Euclidean distance. The relevance of these functions has been tested on the streamflow data at the Thames river at Thamesville. Specifically, three objective functions considering the properties of shape, peak, and gradient of streamflow pattern vectors are suggested. Similar objective functions can be formulated to consider other specific properties of streamflow patterns. AIC, infra and inter distance criteria are reasonable to arrive at an optimal number of clusters for a set of streamflow patterns. The random initialization technique for the K-mean algorithm appears superior, especially when one is able to reduce initialization runs by 20 times to arrive at optimal cluster structure. The streamflow synthesis model is adequate in preserving the essential properties of historical streamflows. However, additional experiments are needed to further examine the utility of the proposed synthesis model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ismail, M.A. and M.S. Kamel (1986) Multidimensional Data Clustering Using Hybrid Search Strategies. Unpublished report, Systems Design Engineering, Univ. of Waterloo.
Kojiri, T., Ikebuchi, S., and T. Hori (1988) “Real-Time Operation of Dam Reservoir by Using fuzzy Inference Theory”. A paper presented at the Sixth APD/IAHR Conf. held at Kyoto, July 20–22, 1988.
Panu, U.S., Unny, T.E. and Ragade, R.K. (1978) “A Feature Prediction Model in Synthetic hydrology Based on Concepts of Pattern Recognition”. Water Resources Research, Vol. 14, No. 2, pp. 335–344.
Panu, U.S., and Unny, T.E. (1980a) “Stochastic Synthesis of Hydrologic Data based on Concepts of Pattern Recognition: I: General methodology of the Approach”. Journal of Hydrology, Vol., 46, pp. 5–34.
Panu, U.S., and Unny, T.E. (1980b) “Stochastic Synthesis of Hydrologic Data based on Concepts of Pattern Recognition: I: Application to Natural Watersheds”. Journal of Hydrology, Vol., 46, pp. 197–217.
Suzuki, E. (1973) Statistics in Meteorology, Modern Meteorology No.5, Chijin-syokan Co., 4m Edition, pp. 254–261, [in Japanese].
Unny, T.E., Panu, U.S., MacInnes, C.D. and Wong, A.K.C. (1981) “Pattern Analysis and Synthesis of Time-dependent Hydrologic Data”. Advances in Hydroscience, Vol. 12, Academic Press, pp. 222–244.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Kojiri, T., Unny, T.E., Panu, U.S. (1994). Cluster Based Pattern Recognition and Analysis of Streamflows. In: Hipel, K.W., McLeod, A.I., Panu, U.S., Singh, V.P. (eds) Stochastic and Statistical Methods in Hydrology and Environmental Engineering. Water Science and Technology Library, vol 10/3. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-3083-9_26
Download citation
DOI: https://doi.org/10.1007/978-94-017-3083-9_26
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4379-5
Online ISBN: 978-94-017-3083-9
eBook Packages: Springer Book Archive