This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area.
<span>Every flood causes damages to many lives and properties. Moreover, it affects the economy and lifestyle of people in the society in a short period and a long period. In consequence, this research would demonstrate techniques and flood detection analyzed through digital images and web application development for receiving reports and inspection of flood situation in every area. The process requires crowdsource data and uses 3S technology so it will receive the accurate and real-time data for making a decision in the flood management and aids to the people in the disastrous area. In this research, Convolutional neural network was applied for flood detection and classification using digital images and data from people or the victims. According to the study, it was found that convolutional neural network for flood classification has accuracy of data at the high level or 95.50%, 93.00%, 97.89%, and 0.91 which are the results of accuracy, producer accuracy, user accuracy, and kappa statistics, respectively. Besides, the use of this technique saves cost, time, and labors. Furthermore, the method could be applied to other disasters such as landslide, earthquake, and fire. It is able to monitor the incident in each type of disasters and also examines the damaged site after the incident</span><em><span>.</span></em>
his research aimed to develop an information system supporting research on rubber (ISSR) in Thailand. This system was designed as a web application with responsive web design. The rubber database was developed with MySQL, used Apache Server as a Web Server and programed with PHP-script. High Chart, Google Chart API and Java Technology were used to represent an online information with graphical format. The system was tested with the actual data on rubber research in Southern Thailand. The system has been available online at URL http://www.s-cm.site/issr. There are three type of users: administrator, researcher (member) and generic user. The researcher performed data entry about research with log-in to the system using username and password provided by the automatic system via online registration form. The administrator can manage the research information. The researchers can manage their research information, use searching tool and leave comments on other member’s research. The generic users can access the system without username and password to view the research and general information on rubber. Moreover, the system generates a report on rubber research with online graphical format. In conclusion, this information system enhances investigation on rubber research in Thailand and its strategy planning for rubber plantation in the future.
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