Predictive Analysis of Airborne Contaminant Spread with Cloud-Connected Sensors and Deep Learning Model

The rapid dispersal of contaminants in the air poses serious threats to human and environmental health. By combining sensors linked to the cloud with a deep learning (DL) model built on Long Short-Term Memory (LSTM) networks, this research proposes a novel way to address this problem. It will help with risk mitigation and proactive decision-making by predicting how airborne pollutants will disperse. The proposed method relies on real-time data gathered from dispersed sensors placed in key areas. These sensors provide data on air quality metrics to a central cloud platform that is analyzed in real-time. Using this data, the LSTM model may be trained to understand the dynamics of contamination distribution over time and identify patterns and relationships. The LSTM model captures complex interactions between pollutant dispersion and various environmental parameters to predict future spread trajectories. The effectiveness of proposed approach is by conducting thorough experiments and validating them against historical contamination data. The considerably outperforms conventional approaches by providing precise and timely estimates of the spread of airborne contaminants. To lessen the impact of possible contamination exposure concerns, these predictions may provide stakeholders with the data need to act swiftly, such as by releasing public health warnings or modifying ventilation systems.