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%.

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.

The objective of this study was to construct the black pepper prices forecasting model by 4 statistical methods: Box-Jenkins method Holt’s exponential smoothing method Brown’s exponential smoothing method and damped trend exponential smoothing method. Time series of monthly black pepper prices which were gathered from the website of the Office of Agricultural Economics during January 2005 to December 2018 of 168 observations were divided into 2 datasets. The first dataset had 156 observations from January 2005 to December 2017 for constructing the forecasting models. The second dataset had 12 observations from January to December 2018 for comparing the accuracy of the forecasts via the criteria of the lowest mean absolute percentage error (MAPE) and root mean squared error (RMSE). Research findings indicated that for all forecasting methods that had been studied the most accurate method was damped trend exponential smoothing method (MAPE = 6.5773 RMSE = 10.3775)

The purpose of this research was to construct the appropriate forecasting model for the prices of field corn in Thailand using four time series analysis methods including Box-Jenkins method Holt’s exponential smoothing method damped trend exponential smoothing method and combined forecasting method. Time series data from the website of the Office of Agricultural Economics with total 227 months during January 1997 to November 2015 are used and divided into two series. The first 216 months from January 1997 until December 2014 used to build the forecasting models and the last 11 months from January until November 2015 used to compare the forecasting methods accuracies via the criteria of the lowest mean absolute percentage error and root mean squared error. Research finding indicated that combined forecasting model was the most accurate.

This objective research to study the possibility of used properties pozzolan material for concrete cement the ternary blend by fly ash metakaolin and silica powder by replacement cement partially on the strength for Flexural and impact by replace Portland cement. Consideration of concrete by varying the ratio there are three values are (w/d) of 0.38 0.55 and 0.8 follow on standards ACI 211.1-91 and variable amounts of replacement cement some. The ternary blend of the fly ash metakaolin and silica powder of percentage 0 15 20 and 25 and by weight of cement. and the control specimens (w/b) without contain fly ash kaolin and silica powder the slump test in the range of 3-5 cm and the use super plasticizer to control the slump test of concrete. By casting specimens of cylindrical diameter 10x20 cm cube 10x10x10 cm and the beam size 10x10x35 cm after curing 28 days and test compressive tensile and flexural strength of concrete all specimens The results showed that The increase will help improve the quality of the concrete physical properties 35 percent up on the cement with clay 15 percent will have the strength to most of the compression is reduced. The increased of compressive and Tension strength as the percentage decrease when replacing cement with fly ash 35 percent up on replacing cement with clay to pull the maximum percentage of 10 bending when replacing cement with clay at a ratio of 10 percent is paid bending. The maximum value of fly ash the flexural increased replace cement in the ratio of 50 per cent are increased the concrete with the most and the last place of cement. As a result of the porosity of the concrete with lower amounts of tightness concrete has increased. Fuzzy logic model is presented for predicting the strength for Flexural and impact by replace Portland cement. The result of the study from experimental case the graph of membership function are present (low medium and high). The results showed that the model Fuzzy Logic. Can be used fuzzy logics to predict as well