Abstract:
In recent years, Dhaka has experienced rapid urbanization, which has intensified sources of air
contamination such as vehicular emissions, industrial activity, and unregulated construction.
These factors have contributed to a noticeable deterioration in air quality. For this causes peoplefaces health problems such as asthma, lung disease, and heart disease, especially amongchildren and old people. In this work, I tried to create a device that could observe and alsopredict air quality in live. For collecting data, I used IoT-based sensors that measure PM2.5,
PM10, CO and some other pollutants. After collecting the data, I experimented with several
traditional and deep learning models, including regression-based, probabilistic, distance-based,
and recurrent neural network approaches. These models are used for predicting air qualitylevels and to see which model gives better results. From the comparison, I found that using amix of models can predict more accurately than using only one model. The system can also givean early warning when pollution is very high. I believe this research will be helpful for Dhakacity planners and authorities so that they can take actions to reduce pollution.
