Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/22181
ชื่อเรื่อง: Development of mobile application for daily air quality assessment in Bangkok
Advisor : Sasivimon Sukaphat
ผู้แต่ง: Rattanaporn Roekphodee
Surirat Yutthasuntorn
Thitiya Sukaphat
Keywords: AQI
GIS
LSTM
PM2.5
วันที่เผยแพร่: 2021
สำนักพิมพ์: Department of Computer Science, Srinakharinwirot University
บทคัดย่อ: Air is an important resource for all living things to live for survival. However, in some area, especially in the capital we have found that the air quality is contaminated with pollution which affects people’s health. Unfortunately, there is still no proper way to deal with the problem of fine dust PM2.5 and this problem becomes a major source of severe environmental air pollution both domestically and internationally. The objective of this research is to propose the fine-tune machine learning models which is able to forecast 7-Day PM2.5 in Bangkok. The model could determine appropriate measures to cope with the haze problem in the future. The Long Short-Term Memory models (LSTM), one of the Deep Learning models, was trained using hourly air pollution data from the Pollution Control Department, Thailand, and The Meteorological Department, Thailand. the experiment results shown that Long Short-Term Memory (LSTM) had the best performance in predictions of PM 2.5 in 7 days. The best results included PM2.5, PM10, Wind Speed, Pressure, Humidity, and Temperature. The model performance values were RMSE 8.47, MAE 6.37 and MAPE 25.19%. This research has improved the efficiency of the model to forecast more accurately by choosing Adam Optimizer.
URI: https://ir.swu.ac.th/jspui/handle/123456789/22181
Appears in Collections:ComSci-Senior Projects

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