In this work solar erythemal ultraviolet radiation (EUV) which affects human skin and skin cancer was investigated at Songkhla province (7.2°N 100.6°E). EUV was converted to UV index then the data was used for model development and forecasting of hourly UV index using Artificial Neural Network (ANN). The ANN model has one input layer two hidden layers and one output layer. This input layer consists of extraterrestrial erythemal ultraviolet radiation solar zenith angle aerosol optical depth and cloud index which affect ultraviolet radiation and the output layer is hourly UV index. The results show that hourly UV index obtained from ANN and that from the measurement are in reasonable agreement with root mean square difference of 12.8% and mean bias difference of -2.4%. For forecasting of the hourly UV index the data for 7 days earlier was used for forecasting the next UV index for one day or nine-hour (08:00 am.-16:00 pm.). Multi-layer perceptron and back propagation algorithm were used in the model forecasting. The results show that the UV index from the model forecasting is reasonable agrees with UV index from measurement with root mean square difference of 17.0% and mean bias difference of 0.3%.

The purpose of this research the study of the potential of Suphanburi area for planting investment. In which the researcher has studied the soil data set that is different in each area of Suphanburi. That is quite diverse if divided by each district in Suphanburi province themselves from the basic information report in Suphanburi and the agricultural information of Suphanburi for the year 2017 when comparing with the soil set of Suphanburi it can be seen that the cultivation is quite comprehensive all. The researcher created a model. Mathematical model to calculate production costs and forecasting of plants suitable for cultivation According to the potential of Suphanburi area. In the form of a website for ease of use as much as possible.

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.