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dc.creatorGhorban, Mohammad Ali
dc.creatorSingh, Vijay P.
dc.creatorSivakumar, Bellie
dc.creatorKashani, Mahsa H.
dc.creatorAtre, Atul Arvind
dc.creatorAsadi, Hakimeh
dc.date.accessioned2017-10-18T15:33:10Z
dc.date.available2017-10-18T15:33:10Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/1969.1/164630
dc.description.abstractA unit hydrograph (UH) of a watershed may be viewed as the unit pulse response function of a linear system. In recent years, the use of probability distribution functions (pdfs) for determining a UH has received much attention. In this study, a nonlinear optimization model is developed to transmute a UH into a pdf. The potential of six popular pdfs, namely two-parameter gamma, two-parameter Gumbel, two-parameter log-normal, two-parameter normal, three-parameter Pearson distribution, and two-parameter Weibull is tested on data from the Lighvan catchment in Iran. The probability distribution parameters are determined using the nonlinear least squares optimization method in two ways: (1) optimization by programming in Mathematica; and (2) optimization by applying genetic algorithm. The results are compared with those obtained by the traditional linear least squares method. The results show comparable capability and performance of two nonlinear methods. The gamma and Pearson distributions are the most successful models in preserving the rising and recession limbs of the unit hydographs. The log-normal distribution has a high ability in predicting both the peak flow and time to peak of the unit hydrograph. The nonlinear optimization method does not outperform the linear least squares method in determining the UH (especially for excess rainfall of one pulse), but is comparable.en
dc.language.isoen_US
dc.subjectGenetic algorithmen
dc.subjectLeast squares methoden
dc.subjectMathematicaen
dc.subjectNonlinear optimizationen
dc.subjectProbabilityen
dc.subjectdistribution functionen
dc.subjectUnit hydrographen
dc.titleProbability distribution functions for unit hydrographs with optimization using genetic algorithmen
dc.typeArticleen
local.departmentBiological and Agricultural Engineering (College of Agriculture and Life Sciences)en
dc.identifier.doi10.1007/s13201-015-0278-y


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