From Indices to Algorithms: A Hybrid Framework of Water Quality Assessment Using WQI and Machine Learning Under WHO and FAO Standards

Abstract

Assessing water quality is essential for the sustainable use of freshwater resources, especially under increasing climatic and agricultural pressures. Small irrigation ponds are particularly sensitive to pollution due to their limited buffering capacity. This study evaluates the water quality of the Ta & scedil;& ccedil;& imath;lar and Yumurtac & imath;lar ponds in Kastamonu, T & uuml;rkiye, by combining conventional Water Quality Indices (WQI) with machine-learning-based interpretation. Physicochemical parameters were measured monthly for one year, and water quality was classified according to WHO and FAO thresholds using the CCME-WQI and weighted arithmetic methods. The integrated approach identified significant differences among standards and highlighted the parameters most responsible for water quality degradation. Machine-learning models improved the interpretation of these indices and supported consistent classification across datasets. The findings emphasize that coupling index-based and data-driven methods can enhance routine monitoring and provide actionable insights for sustainable irrigation-water management, thereby contributing to achieving the SDGs 6, 13, and 15.