Quality of ambient air prediction in Bangladesh - A Time Series Analysis and Machine Learning Approach

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.