Multimodel response assessment for monthly rainfall distribution in some selected Indian cities using best-fit probability as a tool

Abstract

We carry out a study of the statistical distribution of rainfall precipitation data for 20 cites in India. We have determined the best-fit probability distribution for these cities from the monthly precipitation data spanning 100 years of observations from 1901 to 2002. To fit the observed data, we considered 10 different distributions. The efficacy of the fits for these distributions was evaluated using four empirical non-parametric goodness-of-fit tests namely Kolmogorov-Smirnov, Anderson-Darling, Chi-Square, Akaike information criterion, and Bayesian Information criterion. Finally, the best-fit distribution using each of these tests were reported, by combining the results from the model comparison tests. We then find that for most of the cities, Generalized Extreme-Value Distribution or Inverse Gaussian Distribution most adequately fits the observed data.

Best-fit probability distribution models for monthly rainfall of Northeastern Brazil

“…In the last 10 years, several authors have presented different model choices to represent monthly precipitation data (Li et al 2013;Ashkar & Ba 2017;Sukrutha et al 2018;Ye et al 2018;Hasan et al 2019;Nkunzimana et al 2019;Salman et al 2019;Mehdizadeh 2020); these models were listed in Table 1. Among the models, the 2-parameter models presented a better fit and for this reason, we selected the ones which presented the best results to be used in this study, they are Gamma (GAM), Gumbel (GUM), Normal (NORM), Log-normal (LNORM), Generalized Pareto (GP) and Weibull (WEI).…”