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.

Due to the need for Thailand to adapt and develop its economy to keep up with the changes of the world it has to develop technology and innovations with efficiency in order to drive Thailands economy to prosper and keep pace with the rapidly changing global economy. Therefore it is important that educational institutions can help Thailand to have a good economy that is comparable to other countries is to organize the education system with courses that meet the needs of the enterprises. In addition a new generation of entrepreneurs (Start-up) must be created and developed to have knowledge and abilities to drive the countrys economy to be sustainable and efficient. The new generation of entrepreneurs must have knowledge and ability to use technology and innovation in business operations create added value to the economy and bring Thailand into a high income country and people in the country have a better life. Therefore the researcher aims to find guidelines for the development of the Faculty of Science and Technology. Lets step into the development of technology to promote innovation to create a new generation of entrepreneurs with the ability to operate business by using information technology to drive business progress. The samples used to collecte data were personnel students of the Faculty of Science and Technology and establishments related to the occupation of students in both government and private sectors and the research instruments were 2 sets of questionnaires including the 1st set a questionnaire to survey opinions on the development approach of the Faculty of Science and Technology and the second set of questionnaires for enterprises to explore the need for building and develop entrepreneurs in the new era with characteristics that meet the needs of the establishment. The results obtained from the statistical data analysis are guidelines for the development of the Faculty of Science and Technology in creating a proactive strategy to develop the faculty and up-to-date personnel skills training. Access to technology and innovation to step into a university in technology and promote the creation of appropriate innovations to create and develop entrepreneurs effectively.

he monthly rainfall forecasts are very important in Thailand because the most of people has a career in agriculture. The monthly rainfall forecasts can be analyzed for manage water resources. In addition the monthly rainfall forecasting is used to manage water for consume in Thailand. This research is forecasting monthly rainfall under the global warming using a multilayer neural network. It is a network with one hidden layer. The data from the Meteorological Department is use. Imported variables include temperature humidity and wind. The forecast from January to December 2018. The mean square error (MSE) to measure forecasting accurate and found that the value is suitable error (10.25). The neural network can provide accurate monthly rainfall forecasts from January to December. It was found that the southern region had the highest rainfall and the eastern region northeastern region northern region central and western regions respectively. Therefore neural networks can be forecast rainfall accurately and efficiently. It can be used to analyze and manage water resources effective.

Thailand is an agricultural country. So that the water resources are important. The water management is very important for keep the water used in necessary time. The monsoon is causes a heavy rain. The monsoon prediction by the mathematical model is important. The accuracy of the forecasts by the predictability measurement method is very important. In this research improvement the predictability measurement for the winter monsoon prediction in the Southeast Asia 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) are used. The data for run the model on 19 December 2059 from the WCRP CMIP3 is the initial condition. The model is run for 7 days forecast. The Lyapunov exponent (LE) is the predictability measurement method for verify the efficiency of the model and improvement the largest Lyapunov exponent (LLE) by limit theorems for verify the efficiency of the model too. The results to show that the shallow water model can be prediction the winter monsoon for 4 days forecast is accuracy when compare with the BCCR-BCM2.0 model and measure the accuracy by LE. In addition the improvement the LLE by limit theorems can be measure the accuracy of the winter monsoon for 4 days forecast to same as the LE. Therefore the improvement the LLE by limit theorems can be measure the accuracy of the winter monsoon prediction in Thailand by the shallow water model are suitable.

At present the climate is changing due to changing environment. Industrial pollution Smoke from the traffic of cars on the road. Dust from the burning of forests etc. This change makes the climate change rapidly. Affect the temperature rise. In addition the temperature changes also affect the air pressure is changing as well. By the temperature relationship Atmospheric pressure and wind direction change dramatically in the climate such as wind storm precipitation and wind direction. This research is a forecast of air pressure and wind. To analyze the precipitation Wind and storm in the future. This is a summary of wind rain and storm in Southeast Asia including Thailand. The mathematical model used for forecasting is the Global Climate Model which is a powerful model. The initial condition of the forecasts was from the Intergovernmental Panel on Climate Change (IPCC) and the European Center for Medium Range Weather Forecasts (ECMWF) and after forecasting examined or measured the accuracy of the forecasts by Lyapunov exponent (LE) is the most commonly used forecasting method and is the basis of other methods of forecasting accuracy. In addition it is also used to measure the accuracy of the forecast is Finite time Lyapunov exponent (FTLE). Forecasting results show that the initial conditions used in forecasting result in atmospheric pressure and wind direction varying according to different starting conditions. So the temperature the air pressure and the winds cause the rain and the climate change.