Article
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Flood Frequency Analysis Using the Gamma Family Probability Distributions
Version 1
: Received: 17 March 2023 / Approved: 17 March 2023 / Online: 17 March 2023 (09:23:31 CET)
A peer-reviewed article of this Preprint also exists.
Ilinca, C.; Anghel, C.G. Flood Frequency Analysis Using the Gamma Family Probability Distributions. Water 2023, 15, 1389. Ilinca, C.; Anghel, C.G. Flood Frequency Analysis Using the Gamma Family Probability Distributions. Water 2023, 15, 1389.
Abstract
This article presents six probability distributions from the Gamma family with three parameters, for the flood frequency analysis in hydrology. The choice of the Gamma family of statistical dis-tributions was driven by its frequent use in hydrology. In the Faculty of Hydrotechnics, the im-provement of the estimation of maximum flows and including the methodological bases for the realization of a regionalization study with the linear moments method with the corrected pa-rameters was researched, being an element of novelty. The linear moments method is better than MOM because it avoids the choice of skewness depending on the origin of the flows, practiced in Romania. The L-moments method conforms to the current trend for estimating the parameters of statistical distributions. Observed data from hydrometric stations are of relatively short length, so the statistical parameters that characterize them are of a sample that requires correction. The correction of the statistical parameters is proposed, using the method of least squares based on the inverse functions of the statistical distributions expressed with the frequency factor for L-moments. All the necessary elements for their use are presented like, quantile functions, the exact and ap-proximate relations for estimating parameters and frequency factors. A flood frequency analysis case study was carried out for the Ialomita river, to verify the proposed methodology. The per-formance of this distributions is evaluated using Kiling-Gupta and Nash-Sutcliff coefficients.
Keywords
Kritsky-Menkel; Pearson; Wilson-Hilferty; Chi; Inverse Chi; Pseudo-Weibull; estimation parameters; corrected parameters; approximate form; method of ordinary moments; method of linear moments; the method of least squares; confidence interval
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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