Abstract:
Air quality is a key environmental concern, especially in rapidly urbanizing countries such
as Bangladesh, where air pollution poses substantial dangers to public health and the
economy. This study investigates air quality index (AQI) patterns and trends across 13
main cities in Bangladesh, strives to understand the elements that determine air quality,
and attempts to predict future AQI levels using three predictive models: Linear Re-
gression, ARIMA, and LSTM. The study focuses on the ability to estimate AQI values
in diverse urban environments by analyzing these models based on many performance
measures including mean squared error (MSE), root mean squared error (RMSE), mean
absolute error (MAE) and R-squared (R2). This collection, containing historical AQI data
for cities such as Dhaka, Chittagong, Rajshahi and Sylhet in Bangladesh, demonstrates
considerable differences in air quality between locations. The results demonstrate that
LSTM outperforms other models with consistent forecasts and reasonably low error met-
rics across most cities. LSTM achieved the lowest MSEs and RMSEs in numerous urban
regions, confirming its applicability for time-series forecasting of AQI. On the other hand,
linear regression worked effectively in cities where AQI patterns were simpler and more
linear, whereas LSTM, although a more advanced deep learning model, displayed some
issues in managing non-linearity and seasonality in data. The relatively high MAE and
low R2 of the model imply that more refining is required for its effective usage in AQI
forecasting. The study also highlights seasonal and temporal tendencies, such as particular
cities such as Dhaka and Narayanganj having higher pollution levels throughout certain
months, underscoring the necessity for seasonal air quality management. This study shows
the importance of predictive modelling in air quality monitoring and policy making. The
results imply that specific efforts are needed for high-polluting cities, and future studies
should consider hybrid models or more advanced machine learning approaches to boost
the accuracy and usefulness of AQI predictions in Bangladesh all of this I display in my
Website.
