IoT based real time Water Quality Forecasting using Machine Learning and Deep Learning Techniques

Water quality forecasting plays a vital role in environmental sustainability, public health, and smart water resource management. The statistical methods commonly used are not adequate to encompass the nonlinear and dynamic nature of the data. Recently, machine learning (ML) and deep learning (DL) techniques have shown big improvements in predicting water quality parameters like pH, dissolved oxygen (DO), turbidity, biochemical oxygen demand (BOD), and water quality index (WQI). In this article, some of the latest ML and DL methods for water quality forecasting are introduced for the final implementation of the trustworthy and privacy-aware water quality forecasting using federated and explainable AI. The research findings are the IoT sensors enable continuous monitoring in multiple locations and reduce delays. The LSTM outperform traditional machine learning methods in forecasting. This work ensures that authorities and industries can make faster and more informed decisions regarding water treatment, pollution control and resource management.