Published Paper Details:

The amount of pollution caused by humans on the planet has increased dramatically since the industrial revolution. Many of the pollutants in the environment are visible, such as those in the air, water, and soil. Some people, particularly those who reside in big industrial cities, will be aware of air pollution. Since air quality is becoming one of the main factors affecting human health. Air pollution has become a major concern worldwide due to its detrimental effects on human health and the environment. Accurate prediction of air quality is crucial for implementing effective mitigation strategies and safeguarding public health. This study focuses on employing machine learning techniques, specifically the Long Short-Term Memory (LSTM) algorithm, for air quality prediction. The LSTM algorithm, a type of recurrent neural network, is known for its ability to capture temporal dependencies in sequential data. The methodology involves collecting historical air quality data, including pollutant concentrations, meteorological variables, and other relevant factors. These data are preprocessed and used to train the LSTM model, which learns the complex relationships between the input variables and the air quality outcomes. The trained model is then used to make predictions for future air quality conditions. The performance of the LSTM model is evaluated using various evaluation metrics, such as mean absolute error (MAE) and root mean square error (RMSE), to assess its accuracy in predicting air quality.