Abstract
We study the daily precipitation in the municipality of Campina Grande, estimating the parameters of Gamma, Log-Normal, and Weibull distributions. To evaluate the parameter estimators, we compared the Particle Swarm Optimization (PSO) versus Maximum Likelihood Estimation (MLE) to analyze and understand the behaviour of the daily precipitation in Campina Grande. In most cases, our results show evidence that the PSO algorithm is an efficient and robust technique. Notwithstanding, the algorithm also presents an efficient parameter estimation due to its fast convergence.
Highlights
A crucial point to analyze rainfall data is strongly dependent on its distribution pattern
We study the daily precipitation in the municipality of Campina Grande, estimating the parameters of Gamma, Log-Normal, and Weibull distributions
Do Nascimento et al, (2020a) compared the adjustments made between the Weibull distribution with the Method of Moments, with the Maximum Likelihood Estimation and with the Particle Swarm Optimization algorithm, as well as the Lognormal and Weibull adjustments both with the PSO for a wind dataset in the municipality of Petrolina
Summary
A crucial point to analyze rainfall data is strongly dependent on its distribution pattern. It is mandatory to apply a heuristics-based optimization method (Handoyo et al, 2017) Heuristic optimization methods such as simulated annealing (SANN) and PSO have been used to the likelihood function of statistical distributions (Abbasi et al, 2006; Örkcü et al, 2015). Do Nascimento et al, (2020a) compared the adjustments made between the Weibull distribution with the Method of Moments, with the Maximum Likelihood Estimation and with the Particle Swarm Optimization algorithm, as well as the Lognormal and Weibull adjustments both with the PSO for a wind dataset in the municipality of Petrolina. We present the dataset and methodology, we display the results and the discussion, and the conclusions are drawn
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