Adaptive Biofiltration Systems for Urban Pollution Control Using IoT and SVM Models

Adaptive and effective mitigation measures are required due to the increasing levels of urban pollution. To monitor and regulate urban air and water contaminants in real time, this research presents an Internet of Things (IoT)-based biofiltration system combined with Support Vector Machine (SVM) models. The system continually collects data on important pollutants, such as particulate matter, volatile organic compounds, and water pH, using IoT-enabled sensors. The data is then sent to a cloud platform for analysis. This data is processed by an SVM model, which predicts pollution levels and patterns. Data automatically modifies biofilter operations to maximize the removal of pollutants. This adaptive strategy makes sure the biofilter reacts to environmental changes in a dynamic method, optimizing filtering effectiveness across a range of contamination scenarios. To improve monitoring accuracy which minimizes human intervention and promotes autonomous system management. Intelligent biofiltration systems have the potential to greatly help in the sustainable management of urban pollutants, promoting the development of more robust and healthy urban ecosystems.