Multivariate Time Series Analysis and Batch Normalization for Air Quality Prediction in Long Short-Term Memory Networks

The well-being of every individual hinge on the level of oxygen they breathe. The purity of oxygen is intricately tied to the presence of trees in the vicinity, with forests playing a crucial role in enhancing oxygen quality. However, in urban areas, deforestation contributes to elevated air pollutants, leading to adverse health effects in humans. Understanding the sources of air impurities and the specific toxins responsible is essential for individuals and organizations to comprehend the rate of air contamination. This knowledge aids industries and health organizations in predicting and addressing air pollution, ultimately supporting the World Health Organization's efforts. Research focused on air quality data, particularly utilizing Deep Learning approaches, plays a pivotal role in making informed decisions and conducting suitable analyses. The primary objective is to offer users real-time, effective predictions of air quality data using multivariate time series and a BN-LSTM. This combination enables timely and accurate predictions of air quality through Deep Learning. The incorporation of BN-LSTM has notably improved the average magnitude of errors to 28.06. This advancement facilitates enhanced support for predictions by the World Health Organization and other entities concerned with air quality.