Statistical Modeling to Fit Seasonal Rainfall Data from the Doisaket Rain Gauge Station in Thailand - Khamkong, Manad; Pudprommarat, Chookait (Journal of Applied Statistics and Information Technology, 2020)
The statistical modeling of Thai seasonal precipitation data is crucial for the effective planning and management of the country’s hydrological operations. There are two aims of this study: 1) to find an appropriate two-parameter statistical distribution to represent seasonal rainfall data from the Doisaket rain gauge station in northern Thailand and 2) to analyze the effect of seasonality on the rainfall data distribution. Two-parameter distributions, namely Weibull, gamma, lognormal, normal, Lindley exponential, and generalized exponential, were used to determine the best-fitting model of seasonal rainfall data from the Doisaket rain gauge station in Thailand. It was found that the gamma distribution with two parameters was the best fit, as indicated by the minimum values for the Akaike information criterion and the Anderson-Darling goodness-of-fit criterion. In addition, gamma regression showed that the precipitation amount during the rainy season affects that in the cold season. The approach and outcomes of this study could be useful for involved government agencies to strategically plan and manage water resources and to effectively prevent rain-related disasters in the Doisaket area.