Enhancing Air Quality Long Short Term Memory Networks for Predictive Analysis: Insights and Visualization-driven Analysis

An important danger to both the environment and human health is air pollution, necessitating the need for reliable prediction models. This study introduces a novel approach to enhancing air quality prediction using LSTM (Long Short-Term Memory) networks, distinguished for their capacity to extract temporal correlations from sequential data. By employing a large dataset encompassing air quality measurements from various Chinese cities between March 2013 and February 2017, our LSTM-based model demonstrates proficient forecasting of future air quality levels. Furthermore, our research utilizes visualization-driven analysis techniques to deepen understanding of the intricate interplay among air pollution levels, weather conditions, and overall air quality. Interactive visualizations facilitate comprehension and data interpretation, enabling a comprehensive grasp of the dynamics of air pollution. In our experiments, the LSTM-based technique surpasses established methods, showcasing its potential to support decision-making processes for policymakers and environmental stakeholders. By increasing the predictability of air quality and providing insights into pollution dynamics, our research contributes to the formulation of effective strategies for managing air pollution and safeguarding public health.