Volume 11 Issue 1 - July 2018

  • 1. An assessment of predictive accuracy for regional flood frequency distribution estimation methods on awash river basins

    Authors : Habtamu Ketsela Mengistu, Kamatchi Sivakumar

    DOI : http://dx.doi.org/10.21172/1.111.06

    Keywords : Best-fit statistical distributions, Easy fit, L-moment, Parameter estimation methods, Kolmogorov-Smirnov test

    Abstract :

    The most important face of flood from water resources improvement and management part is its returning interfering with interventions and actions made by people. The loss of life and damage can be pictured in terms of economic dead and risk to human life. The main concern is here to significantly analyze the occurrence and amount of the flooding intervention.The main objectives of this study includes identifying the best-fit statistical distributions to the data of each gauge and finding a suitable parameter estimation method for each station regions of Awash river basin. The l-moment and easy fit software was employed for selection of best-fit distributions and methods of parameters estimation for a station. Goodness-of-Fit tests such as Chi-square, Anderson-Darling and Kolmogorov–Smirnov are applied for checking the satisfactoriness of fitting of probability distributions to the recorded data. Kolmogorov–Smirnov test is used for the choice of a suitable distribution for estimation of maximum flood discharge. The performance of regional General Extreme value, General Pareto and Uniform distributions are found to be highly satisfactory and widely applied in this paper, however this paper reveals that the General Extreme Value distribution is better appropriate amongst seven distributions used in the estimation of maximum flood discharge at Awash River basins.

    Citing this Journal Article :

    Habtamu Ketsela Mengistu, Kamatchi Sivakumar, "An assessment of predictive accuracy for regional flood frequency distribution estimation methods on awash river basins", https://www.ijltet.org/journal_details.php?id=934&j_id=4653, Volume 11 Issue 1 - July 2018, , #ijltetorg