Advanced Water Quality Prediction: Leveraging Genetic Optimization and Machine Learning

Water quality monitoring is essential for public health, environmental sustainability, and effective resource management. This study introduces an innovative approach that combines machine learning techniques with genetic optimization algorithms to predict water quality with high accuracy. By analyzing a dataset containing attributes such as pH, turbidity, and hardness, the proposed system achieves a remarkable 97 accuracy. Key metrics like precision, recall, and F1-score validate the robustness of the model. Comparative analysis with existing methods highlights the advantages of this approach. This work addresses challenges like data variability and ensures practical applicability, offering a reliable solution for water quality assessment and management.