The study of drought factors to analyze and manage water is very important. The main contributing factor to drought is unusual summer weather wind pressure and air mixtures include water vapor and water droplets. In this factors can be analyzed for rainfall in the lower central river basin area and the preliminary data of the research used to analyze drought from Thai Meteorological Department (TMD). This research analyzes the factors that cause drought including rainfall temperature pressure wind and precipitation. The Multiple linear regression analysis used to determine the relationship of factors causing drought. It was found that rainfall had a statistically significant relationship with temperature and pressure. In addition using the Geographic Information System (GIS) an analysis of rainfall in the lower central river basin from October 1920 to September 2021 to found that from August to October there is a lot of heavy rainfall according to the multiple linear regression equation. Therefore the created multiple linear regression equations can be effectively used in the analysis of drought areas in the lower central river basin area.

The objectives of this research were to propose a spatial analysis using Bayesian generalized linear mixed model for predicting drought in Thailand. The spatial effect was assumed to be a conditional autoregressive model (CAR model). The dependent variables was a drought or non-drought occurrence which was assumed to have a Bernolli distribution with logistic link function. Factors considered were rain temperature number of rainy days number of large reservoirs and number of medium reservoirs. The results show that the rain temperature number of rainy days number of large reservoirs and number of medium reservoirs influence on the drought occurrence. This study is useful for planning making decision preventing and remedying the drought problem.

The objectives of this research are to propose a proper model for risk assessment for drought in Thailand. A hidden Markov model is adopted. The results indicate that the amount of rainfall depends on the hidden states drought or non-drought state. If the state changes from drought to non-drought the amount of rain will increase. The probability of changing from drought state to non-drought state is higher than from drought state to drought state. The probability of drought occurrence in February is highest followed by in November March December January July April June October September August and May respectively. When the temperature increases the probability of drought occurrence will increase.