This research study about rainfall pattern in Thailand due to the influenced of the tropical cyclone. The annual rainfall analysis was derived from the Thai Meteorological Department (TMD) since 1 9 7 1 -2 0 0 0. The analysis of annual rainfall used composite analysis and classification of rainfall patterns with cluster analysis. To measuring the similarity between the rainfall patterns in each year by hierarchical clustering method. In addition use to Single Linkage rules for grouping the rainfall pattern and using the distance between the units close together as possible into consideration. The summarized of rainfall pattern due to the influenced of the tropical cyclone are usually found in the eastern and northeastern regions of Thailand. It is found that a heavy rainfall. The rainfall pattern in the western regions of Thailand has the least amount of rainfall as a result of drought.

Weather forecasts are very necessary to predict the future weather conditions. The important thing in forecasting is the model and variables. The model used in the forecasting must be accurate and effective. In addition the variables used in forecasting must be comprehensive and appropriate to be able to represent the forecast. The initial conditions for forecasting must be appropriate and reliable. There are many methods to generate the initial conditions in forecasting. To generate the initial conditions for winter monsoon prediction with Kalman Filtering method which is a method used to predict the status of various systems and able to create values of variables used in forecasting to have a variety of similarities can be used as a starting variable in forecasting very well. In this research the winter monsoon prediction by the Shallow Water Model and validating the initial conditions by Root Mean Square Error (RMSE). The result of weather prediction in the winter monsoon can be used the Kalman Filtering method for generate the initial conditions.

The purpose of this research is to study the Rajamangala University of Technology Phra Nakhon student activities information system usage and payoff if it meets the requirements of the university in order to keep track of the students extra curriculum activities and to study the user satisfaction usage and payoff of the system. 385 samples were systematically randomly selected among 10153 students studying in the undergraduate class year 1 to year 3 and the personnel working in all Faculties of the university at the 95% confidence level. The study found the following results: The majority of the respondents mainly were female 54.5 percent 96.4 percent were students studying in year 1 41.3 percent and from Business Administration Faculty 36.9 percent. Overall satisfaction toward the student activities information system was at a high level with an average of 3.66. Overall usage of the student activities information system was at a high level with an average of 3.74 and overall payoff of the student activities information system was at a high level with an average of 3.77. The hypothesis testing showed that different personal factors including gender occupation/status class and faculty did not varied the satisfaction toward the student activities information system statistically significant at the .05 level. Different personal factors including gender occupation/status class and faculty did not varied the usage of the student activities information system statistically significant at the .05 level. Different personal factors including gender occupation/status class and faculty did not varied the payoff of the student activities information system statistically significant at the .05 level. Satisfaction toward the student activities information system including system quality information quality and service quality is positively related to the usage of the student activities information system statistically significant at the .05 level and the student activities information system usage is positively related to the student activities information system payoff statistically significant at the .05 level.

The forecasting method is important because it can predict phenomena in the future. The accuracy of the forecast depends on the model and the initial conditions. In addition the predictability measurement method is important can be check the accurate of forecasts. In this research is winter monsoon forecasts in Thailand by the shallow water model. The data from The Bjerknes Centre for Climate Research (BCCR) University of Bergen Norway. The global climate model is Bergen Climate Model (BCM) Version 2.0 (BCCR-BCM2.0) of the Intergovernmental Panel on Climate Change (IPCC) is used. The Lyapunov exponent (LE) is the predictability measurement method for verify the efficiency of model and establish the new predictability measurement method by the time series analysis. The result to show that the new predictability measurement method by the time series analysis can be measure the efficiency of the winter monsoon forecasts in Thailand by the shallow water model for December 2015 to December 2056 are suitable.

The shallow water model is a mathematical model was developed from the shallow water equations. It consists of the continuous flow and the momentum equation of non-linear in the x-axis and y-axis. The boundary conditions of the model is the cyclic continuity from east to west and the open boundary conditions from north to south. The northeast monsoon forecast by the shallow water model can’t be predicted in a long time period because the boundary conditions of the model has a problem. Thus improvement the boundary conditions of shallow water model by using wall boundary condition from north to south. The results of this research to found that the shallow water model can correctly forecast the northeast monsoon period in the long term.