Advanced Machine Learning Techniques for Predicting Water Quality and River Flow Dynamics

Precise forecasting of water quality and river dis-charge is fundamental for sustainable water resource steward-ship, environmental protection, and public well-being. Conventional predictive approaches habitually unable in capturing the complicated and dynamic nature of hydrological systems. In recent times, machine learning algorithms have emerged as robust instruments to tackle these challenges, enabling more accurate and dependable predictions through the examination of extensive datasets. This paper explores the utilization of diverse ML models, encompassing artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and cutting-edge deep learning models such as convolutional neural networks (CNN). Our assessment of these models underscores substantial enhancements in prediction accuracy compared to traditional approaches, especially when employing ANN and deep learning models. The insights gleaned from this review emphasize the transformative potential of machine learning in water resource management and propose avenues for future research in this vital field.