Advanced IoT Framework for Water Pollution Monitoring and Prediction
TL;DR
An end-to-end Internet of Things framework designed for real-time water quality monitoring and predictive pollution modeling and a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed.
Abstract
Water pollution poses a severe threat to global public health, aquatic biodiversity, and sustainable resource management. Traditional monitoring methods rely on manual sample collection and laboratory analysis, which are labor-intensive, time-consuming, and fail to provide early warning capabilities. This paper proposes an end-to-end Internet of Things (IoT) framework designed for real-time water quality monitoring and predictive pollution modeling. The framework integrates a network of low-power, multisensor edge nodes deployed across aquatic bodies to measure key parameters including pH, turbidity, dissolved oxygen (DO), total dissolved solids (TDS), and temperature. Data collected from the sensor layer is transmitted via low-power wide-area network protocols (LoRaWAN/MQTT) to a centralized cloud analytics platform. To enable proactive environmental management, a hybrid machine learning architecture—combining Long Short-Term Memory (LSTM) networks for time-series forecasting and Random Forest models for anomaly classification—is proposed to predict spatial-temporal pollution trends and identify illegal dumping events before severe contamination occurs. This paper proposes a conceptual architecture intended to guide future implementation and validation Keywords: machine learning; edge computing; Internet of Things (IoT); Water Quality Monitoring; Time-Series Prediction; Environmental Sensing; LoRaWAN