Establishment of an innovative machine learning-driven drinking water quality assessment model with health considerations.

With increasing resident incomes, public attention has increasingly focused on the health impact of drinking water. However, the development of evaluation systems for ensuring healthy drinking water remains limited globally. This study proposed an innovative drinking water quality index (DWQI) model that integrates health considerations, advanced parameter selection, and enhanced methodologies for water quality assessment. Utilizing k-means clustering, 16 key parameters were identified. Four combined weight optimization schemes focused on health impacts were developed, with the CWAR scheme, combining analytic hierarchy process and random forest methods, identified to be optimal. The ideal value concept was introduced alongside the calculation of sub-indices to incorporate health impacts. An evaluation of finished water quality from drinking water treatment plants in Zhejiang Province (2018-2023) revealed that 90.93 % of DWQI values met or exceeded Class II standards. Significant correlations were observed between water quality and factors such as the proportion of the primary industry (-0.33) and urban residents' income (0.17). The management of industrial wastewater discharge under the "Five Water Cohabitation" policy had a significant impact on the improvement of DWQI values. Finished water obtained from advanced treatment showed notably better quality than those obtained from conventional and mixed treatments. DWQI scores of finished water from major cities across provinces in China indicated that 60.87 % of the finished water was classified as Class II. Overall, this model provides a framework for assessing drinking water quality and enhances water environment management by guiding policies and optimizing treatment processes, thereby ensuring safer drinking water for communities.