Regional Frequency Analysis of Extremes Precipitation Using L-Moments and Partial L-Moments

Extremes precipitation may cause a series of social, environmental, and ecological problems. Estimation of frequency of extreme precipitations and its magnitude is vital for making decisions about hydraulic structures such as dams, spillways, and dikes. In this study, we focus on regional frequency analysis of extreme precipitation based on monthly precipitation records (1999–2012) at 17 stations of Northern areas and Khyber Pakhtunkhwa, Pakistan. We develop regional frequency methods based on L-moment and partial L-moments (L- and PL-moments). The L- and PL-moments are derived for generalized extreme value (GEV), generalized logistic (GLO), generalized normal (GNO), and generalized Pareto (GPA) distributions. The Z-statistics and L- and PL-moments ratio diagrams of GNO, GEV, and GPA distributions were identified to represent the statistical properties of extreme precipitation in Northern areas and Khyber Pakhtunkhwa, Pakistan. We also perform a Monte Carlo simulation study to examine the sampling properties of L- and PL-moments. The results show that PL-moments perform better than L-moments for estimating large return period events

A new fitness-based selection operator for genetic algorithms to maintain the equilibrium of selection pressure and population

A genetic algorithm is one of the best optimization techniques for solving complex nature optimization problems. Different selection schemes have been proposed in the literature to address the major weaknesses of GA i.e., premature convergence and low computational efficiency. This article proposed a new selection operator that provides a better trade-off between selection pressure and population diversity while considering the relative importance of each individual. The average accuracy of the proposed operator has been measured by χ2 goodness of fit test. It has been performed on two different populations to show its consistency. Also, its performance has been evaluated on fourteen benchmark problems while comparing it with competing selection operators. Results show the effective performance in terms of two statistics i.e., less average and standard deviation values. Further, the performance indexes and the GA convergence show that the proposed operator takes better care of selection pressure and population diversity

Empirical E-Bayesian estimation of hierarchical poisson and gamma model using scaled squared error loss function

The hierarchical models have not only a major concern with developing computational schemes but also assist in inferring the multi-parameter problems. The E-Bayesian is the expected Bayesian estimation that can be found by taking the integrals of Bayesian estimator using a hyper-prior with respect to the hyper-parameters. This study introduces the empirical E-Bayesian estimation that is coalesced with hierarchical modeling which prior to this has not been investigated. The scaled squared error loss function (SELF) has been used to estimate the parameter of Hierarchical Poisson-Gamma (HPG) model using empirical E-Bayesian estimation. The empirical E-Posterior risk is considered to be the evaluation standard. In addition, the consistency along with the asymptotic normality of the posterior distribution have been discussed. Furthermore, the empirical Bayes method is used to estimate the values of hyper-parameters via Maximum Likelihood (ML) method. The Monte Carlo simulation is executed to assess the precision of proposed estimators and a real-data application has been analyzed for illustration and comparison purposes

Development of some useful generators to obtain partially neighbor balanced designs

Neighbor balanced designs are robust to neighbor effects, therefore, these designs are used to balance out the neighbor effects. If a large number of experimental material is required for combinatorial neighbor balance then partially neighbor balanced designs should be recommended. In this study, some useful generators are developed to obtain the partially neighbor balanced designs in linear blocks of sizes 3–7. Keywords: Linear block, Neighbor effects, Neighbor balanced designs, Partially neighbor balanced design

vs under different distributions and values of <i>ρ</i> and <i>p</i> when <i>m</i> = 4.

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vs under different distributions, values of <i>ρ</i> and <i>p</i> when <i>m</i> = 5, <i>c</i>_<i>q</i> = 20and<i>c</i>_<i>s</i>=20 and <i>c</i>_<i>s</i> = 2.

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vs under different distributions, values of <i>ρ</i> and <i>p</i> when <i>m</i> = 4, <i>c</i>_<i>q</i> = 20and<i>c</i>_<i>s</i>=20 and <i>c</i>_<i>s</i> = 2.

(TIF)</p