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.

This research aims to study the website link analysis of Faculty of Engineering RMUTP both of the internal link or the external link via the HTTP standard response code of all 9 under graduate departments web pages. From reasons of the web developers create and modify some part of content such as to include more the pieces of information or to exclude its. The website data collection period started from February 2018 to May 2018 via several directory paths or link-paths and the author found 404 (Not Found) most often than 405 (Method Not Allowed) and 504 (Gateway Timeout) in order on average 78 percent. Totally the links into and out of the engineering faculty website approximately 7500 connections.

The objectives of this research are to propose an efficient and proper model that fits the cassava and rubber yields data. A generalized estimating equation (GEE) and a linear mixed model (LMM) with spatial correlation following the conditional autoregressive model (CAR) were adopted. The dependent variables are the cassava and rubber yields collected each month in every province of Thailand. The factors considered are rainfall averaged temperatures and regions. The results from GEE and LMM show that the factors influencing on the cassava and rubber yields are rainfall averaged temperature and region. Both GEE and LMM fit the correlated data. The GEE is used to explain the influence of factors on the yields in all provinces while the LMM is used to explain the influence of factors on the yields in each province.

The objectives of this research are to propose a linear mixed model including spatial effects to present the distribution of air pollution of each gas being Sulphur dioxide (SO2) Nitrogen dioxide (NO2) Carbon monoxide (CO) and Ozone (O3) and particulate matter (PM10) and to investigate the factors related to those air pollution in each area of Bangkok Metropolitan Region. The spatial effects are assumed to be a conditional autoregressive (CAR) model. The dependent variables are the amount of SO2 NO2 CO O3 and PM10 in each area and the independents considered are Industrial area business area and suburb area. The results indicate that the factors influencing the amount of air pollution ranking from most to least are industrial area suburb area and business area respectively.

This research proposes technical performance evaluation and economic decence for the combination of harmonic correction devices in Industry. Currently an electronic device is popularly applied in power network. Although it is a higher performance device than the existing device it still causes the power quality issues and then network failure. Especially harmonic disturbance in the network it is a harmonic source that generates harmonic current into the network. Many types and solutions are available in the present time therefore the suitable and optimal financial solution must be considered. A network model for the analysis of harmonic correction devices suitable solutions and optimal financial has been established in the simulation software that bases on the power and Break Even Point (BEP) equations. The results of analysis shown that the performance of harmonic correction devices suitable solution and BEP of investment in match to the using of a detuned filter bank and an active harmonic filter (case 8) thus these information will be valued for the end-user.