An Integrated Machine Learning Framework for Real-Time Water Quality Monitoring

Machine Learning for Soft Sensing in Water Quality Monitoring: A Survey and Tutorial

Water quality monitoring is essential for environmental protection, public health, and sustainable water resource management. However, many important water quality parameters remain difficult or expensive to measure continuously using conventional sensing techniques. In recent years, Machine Learning (ML)-based soft se...

A unified LSTM autoencoder–random forest AI-IoT framework for real-time water quality monitoring, anomaly detection, and water quality index prediction

From low-cost measurements to rapid water quality monitoring: a hybrid machine learning-optimization framework for predicting laboratory-intensive parameters.

Reliable estimation of laboratory-intensive water-quality variables remains challenging in spatially heterogeneous river systems. This study developed a machine-learning framework for predicting BOD, COD, DO, NH₄, NO₂, NO₃, and TA across major rivers of Khuzestan Province, Iran, using a 12-year monitoring dataset. Afte...

Data-driven air quality prediction in Navi Mumbai city using machine learning techniques

Monitoring and Temporal Prediction of Groundwater Quality using data Obtained via Probe sampling

Despite advances in microelectronics and IoT (Internet of Things) that enable low-cost telemetry sensors, most environmental agencies still operate with periodic in situ sampling campaigns, characterized by sparse, irregular data and a high rate of missing values. While analytically reliable, this data was designed for...

Internet of Things-Enabled Water Quality Monitoring System.

Water quality monitoring protects human health, aquatic habitats, and sustainable water resource management. Manual sampling and laboratory testing are laborious, time-consuming, and unsuited for continuous water quality monitoring. A proof-of-concept IoT-Enabled Water Quality Monitoring System (IoT-WQMS) that uses an...