A Machine Learning-Driven Framework For Real-Time Environmental Pollution Monitoring And Prediction Using Iot And Remote Sensing Data

Abstract

The rapid pace of industrialization and urbanization has led to severe degradation in air and water quality across the globe. Traditional environmental monitoring systems are often reactive, fragmented, and slow in responding to pollution incidents. This research presents a novel, integrated machine learning-driven framework that leverages real-time data from Internet of Things (IoT) sensors and remote sensing platforms to monitor, predict, and assess environmental pollution levels. The system utilizes a hybrid architecture combining spatial (satellite) and local (sensor-based) data sources to feed predictive models such as Random Forest, XGBoost, and LSTM for real-time assessment of air and water quality parameters, including PM2.5, PM10, NO₂, SO₂, pH, and turbidity. A cloud-based processing pipeline is employed to collect, preprocess, and analyze streaming data, while geospatial analysis is used to generate pollution heatmaps. Experimental evaluations conducted on multi-city datasets from the Central Pollution Control Board (CPCB), Sentinel-5P, and open-source IoT deployments demonstrate a prediction accuracy of over 92% and timely alerts for environmental threshold violations. The results confirm the potential of this hybrid approach in enabling proactive environmental management and policy-making through sustainable data-driven insights