Abstract
Water resources planners and managers use historic monthly streamflow data for a variety of purposes. Often, the data set is not complete and gaps may exist due to various reasons. This paper develops and tests two computer models for infilling the missing values of a segment. The first model utilizes data only from the series with a segment of missing values, whereas the second model utilizes data from the series with a segment of missing values as well as from other concurrent series without a segment of missing values. These models are respectively referred to as the Auto-Series (AS) model and the Cross-Series (CS) model. Both models utilize the concepts of seasonal segmentation and cluster analysis in estimation of the missing values of a segment in a set of monthly streamflows. The models are evaluated based on comparison of percent differences between the estimated and the observed values as well as on entropic measures.
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References
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© 1994 Springer Science+Business Media Dordrecht
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Goodier, C., Panu, U. (1994). Infilling Missing Monthly Streamflow Data Using a Multivariate Approach. 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_15
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DOI: https://doi.org/10.1007/978-94-017-3083-9_15
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4379-5
Online ISBN: 978-94-017-3083-9
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