Multimodel Response Assessment for Monthly Rainfall Distribution in Some Selected Indian Cities Using Best Fit Probability as a Tool

TL;DR

This study identifies the best-fit probability distributions for monthly rainfall data in 20 Indian cities over a century, using multiple statistical tests to evaluate the fit and determine the most suitable models.

Contribution

The paper introduces a comprehensive approach combining multiple goodness-of-fit tests to determine the best-fit rainfall distribution models for Indian cities.

Findings

Generalized Extreme-Value Distribution fits most cities' data

Inverse Gaussian Distribution is also a good fit for several cities

Multiple statistical tests effectively identify optimal distribution models

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.