Enhancing Water Quality Management Utilizing Machine Learning Process

Abstract Agricultural drainage water reuse has emerged as a pivotal strategy to mitigate the growing imbalance between water supply and demand in Egypt. This study focuses on effectively managing the water quality in the Gharbia Drain to ensure its suitability for reuse in irrigation. Tailored Water Quality Indices (WQIs) were developed utilizing Artificial Neural Networks (ANNs) to assess biological, industrial, and agricultural pollution, based on the analysis of 19 key water quality parameters collected over multiple years and seasons. The WQIs generated were benchmarked against the Canadian Water Quality Index (CWQI) to ensure accuracy and reliability. Additionally, scenarios for improving water quality across six branch drains were evaluated, and actionable solutions were proposed. Decision tree models identified key pollution indicators and recommended appropriate treatment actions. The decision tree enabled objective water quality management by identifying the most effective treatment scenarios. It provided a clear, data-driven framework for selecting the best implementation strategy. The decision tree enabled objective water quality management by identifying the most effective treatment scenarios. It provided a clear, data-driven framework for selecting the best implementation strategy.Furthermore, treatment scenario evaluations revealed that secondary treatment proves highly effective for upstream branch drains, enhancing water quality to "Good" or "Excellent" classification, while primary treatment works well for downstream branch drains.This comprehensive approach highlights the value of machine learning in water quality assessment, decision-making, and sustainable resource management.