This study explores the complexity of assessing river water quality, a multifaceted process influenced by various water quality parameters (WQPs), inherent uncertainties, and diverse decision-maker judgments. These uncertainties are effectively modeled using fuzzy sets and soft sets. We introduce a novel weighted water pollution score function (WWPSF) on fuzzy soft sets (FSSs) and propose an innovative fuzzy multi-criteria decision-making model (FMCDMM) to derive a weighted water pollution score (WWP-score) for rating river water pollution. We apply this methodology to assess water quality indices for the Manu River, the longest river in Tripura, India, and a crucial drinking water source for North-East Tripura. The FMCDMM and WWP-score are key indicators of the river’s water quality. To assess water quality, we consider ten crucial parameters: pH, calcium, dissolved oxygen, total alkalinity, biochemical oxygen demand, total hardness, total dissolved solids, chloride, total coliform, and fecal coliform, sampled at eight strategic sites along the river during pre-monsoon, monsoon, and post-monsoon seasons of 2021–2022. Guided by waste discharge points, we integrate meteorological data, land-use patterns, and anthropogenic inputs to reveal significant water quality degradation due to seasonal variations and human activities. Factors such as rainfall intensity, temperature fluctuations, industrial discharges, agricultural runoff, and urban waste contribute substantially to pollution levels. This comprehensive approach aids decision-makers in developing strategies for sustainable river management, emphasizing the need to address seasonal changes and anthropogenic impacts to ensure water security for urban ecosystems and dependent communities. Finally, we conduct comparative analyses with existing methodologies to validate and highlight the advantages of our approach.
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