การประมาณค่าความหนาแน่นของ PM 2.5 ด้วยการเรียนรู้ของเครื่อง - ศีลวัต หมื่นสุวรรณ (จุฬาลงกรณ์มหาวิทยาลัย, 2020)
The topic of this research is "A Density Estimation of PM 2.5 using Machine Learning". The objectives of this research are to define important factors for PM 2.5 and to develop relationships between considered factors and PM 2.5. The aim of this research is to determine factors those affect the risk-level of PM 2.5 as high or low. The research scopes on the the historical data of PM 2.5 values in the year 2020 only, and the data sources are the National Statistical Office, Meteorological Department, Pollution Control Department, Thai Meteorlogical Department website, Air Quality Historical Data Platform website, and Google Earth. The research method aims to analyze the collected data of PM 2.5 to find the best volume-prediction model of the PM 2.5 pollution using RapidMiner Studio. The results have shown that factors affecting PM 2.5 those support or push the danger level are female populations of the province, precipitation, female populations of the district, air pressure at sea level, and metres above sea level. In contrast, the total population of the district, male populations of the district, season, temperature, and humidity are all factors that can decrease the hazard level of PM 2.5.