Harnessing Machine Learning for IoT-Driven Atmospheric Parameter Monitoring and Predictive Analytics

The ecology and human health are seriously endangered by air pollution. Due to the expensive expense of the monitoring equipment, Air quality surveillance hubs are sometimes restricted to few places. They give a sketchy representation of the city's air quality and ignore regional variances. Arrayed sensor networks can now be used to assess embracing timely environmental insights at numerous places using affordable sensor innovations and wireless connectivity revolutionizing like Internet of Things (IoT). This article delineates inception on innovative and cost-effective sensor nodes that measures the concentrations of Carbon Monoxide (CO), Nitrogen Dioxide (NO2), and Particulate Matter (PM) using inexpensive Electrochemical Sensors (MQ- series).For field calibration, the generated sensor nodes were placed next to a precise reference CO sensor. Data from the inexpensive sensors robust correlation is calibration with offset and gain. Acknowledged data of sensors can be monitored continuously through a IoT webpage far away from the module. Random Forest(RF) classifier based calibration shows a precision of 82.84% Exploring the relationship between sensor data and Artificial Neural Network (ANN)-based Multi-Layer Perceptron calibration has a precision of 76.05%.Can do various test as a catalyst for further improvement.