Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Probability Weighted Moments–Based Parameter Estimation for Kinematic Diffusion and Muskingum-Based Distributions
Hydrologic series in arid and semiarid areas often contain zero values. For such data, the unit impulse response function (irf) of a kinematic diffusion model and a Muskingum flood routing model have been employed as probability distribution functions for frequency analysis. However, the probability weighted moments (PWMs) of these distributions have not been derived for parameter estimation. In this study, the parameter estimation formulas of a two-parameter kinematic diffusion distribution (KD2), a two-parameter Muskingum-based distribution (M-like), and three Muskingum-based three-parameter distributions based on PWMs are derived using mathematical transformation and numerical calculation principles. The fitting effects of PWMs of these distributions are evaluated and compared with the method of moments (MOM) and maximum likelihood method (MLM) using ordinary least square (OLS) criterion, residual square sum criterion (RSS), and Akaike information criterion (AIC). Precipitation data collected from six gauging stations were used as a case study. Results show that for each distribution, PWM yields the smallest OLS, RSS, and AIC values among the three methods and improves the accuracy of estimation.
Probability Weighted Moments–Based Parameter Estimation for Kinematic Diffusion and Muskingum-Based Distributions
Hydrologic series in arid and semiarid areas often contain zero values. For such data, the unit impulse response function (irf) of a kinematic diffusion model and a Muskingum flood routing model have been employed as probability distribution functions for frequency analysis. However, the probability weighted moments (PWMs) of these distributions have not been derived for parameter estimation. In this study, the parameter estimation formulas of a two-parameter kinematic diffusion distribution (KD2), a two-parameter Muskingum-based distribution (M-like), and three Muskingum-based three-parameter distributions based on PWMs are derived using mathematical transformation and numerical calculation principles. The fitting effects of PWMs of these distributions are evaluated and compared with the method of moments (MOM) and maximum likelihood method (MLM) using ordinary least square (OLS) criterion, residual square sum criterion (RSS), and Akaike information criterion (AIC). Precipitation data collected from six gauging stations were used as a case study. Results show that for each distribution, PWM yields the smallest OLS, RSS, and AIC values among the three methods and improves the accuracy of estimation.
Probability Weighted Moments–Based Parameter Estimation for Kinematic Diffusion and Muskingum-Based Distributions
Wei, Ting (Autor:in) / Song, Songbai (Autor:in)
28.09.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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