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    PM 2.5 Prediction & Air Quality Classification UsinMachine Learning
    (2024-06-01)
    Soontornpipit, Pichitpong
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    Lekawat, Lertsak
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    Tritham, Chatchai
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    Tritham, Chattabhorn
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    Pongpaibool, Pornanong
    Forecasting plays a vital role in air pollution alerts and the management of air quality. Studies and observations conducted in Thailand indicate a concerning rise in pollution levels, particularly in the concentration of PM2.5. concentrations. Bangkok, in particular, has been flagged for its alarmingly high PM2.5 By projecting the future PM2.5 concentrations in these urban areas, we can obtain valuable short-term predictive information regarding air quality. After conducting experiments using four different machine learning algorithms, it was found that the LSTM (Long Short-Term Memory) model provides the most accurate forecasts based on various statistical evaluation indicators. These indicators include a Root Mean Square Error (RMSE) of 2.74, Mean Absolute Error (MAE) of 1.97, R-squared value of 0.94, and Mean Absolute Percentage Error (MAPE) of 10.53. Then the classified air quality based on PM2.5 from the LSTM model gives the best performance indicators including accuracy = 0.9072, precision = 0.8466, negative predict value = 0.9403, sensitivity = 0.8144, specificity = 0.9381, and F1-score = 0.8169. The results show that the machine learning model can predict PM2.5 concentration, which is suitable for early warning of pollution and information provision for air quality management systems in Bangkok.
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    Forecasting Commercial Vehicle Demand Using a Multiple Linear Regression Model
    (2023-01-01)
    Aungkulanon, Pasura
    ;
    Hirunwat, Anucha
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    Atthirawong, Walailak
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    Luangpaiboon, Pongchanun
    The purpose of this study was to develop predictive equations and explore the elements that are having an effect on the demand for commercial vehicles in Thailand. The Consumer Price Index (CPI), the Business Sentiment Index (BSI), the price of diesel fuel, and the desire for electric cars were all factors that were considered while compiling this information from a database of newly registered automobiles. Multiple regression analysis, consisting of a linear model and a quadratic model, was utilized to investigate how the presence of a variety of influences influenced the demand for commercial cars. Both the Consumer Price Index and the relationship between the CPI and diesel fuel costs were shown to have a considerable impact on the demand for commercial vehicles. This was the case regardless of which factor was considered first. It was determined that there was a connection between these two parameters of 74.96% after doing the necessary calculations.