An innovative fuzzy multi-criteria decision making model for analyzing anthropogenic influences on urban river water quality

This study addresses the complexity of assessing river water quality, a multifaceted process influenced by numerous water quality parameters (WQPs) characterized by inherent uncertainties and diverse judgment information from decision-makers. These uncertainties and diverse judgment information can be effectively represented and simulated using fuzzy sets, soft sets, and fuzzy soft sets (FSSs). In this study, we introduce the root mean square (RMSQR) operation and a relative weighted score function (RWSF) on FSSs. Using these innovative concepts, we propose an effective water-pollution rating system, the fuzzy multi-criteria decision-making model (FMCDM-model), to derive a relative weighted water-pollution score (ϖ-score) for rating water-pollution levels. We apply our methodology to assess water quality indices for the Haora River in Tripura, India. This river is crucial as it is the primary drinking-water source for Agartala, the capital city of Tripura. Originating from the Baramura range at 247 m altitude, the 47.21-km long Haora River flows westward through the plains, joining the Titas River in Bangladesh. For assessing water quality, we consider 15 crucial WQPs, namely pH, Color, Electrical conductivity, Eotal dissolved solids, Total suspended solids, Total alkalinity, Total hardness, Chemical oxygen demand, Biochemical oxygen demand, Dissolved oxygen, Calcium, Magnesium, Chloride, Total coliform, and fecal coliform, sampled at seven strategic sites along the river. Sampling was conducted across the pre-monsoon, monsoon, and post-monsoon seasons from 2023 to 2024, guided by the distribution of waste-discharge points. Finally, we conduct comparative analyses with existing methodologies to validate and highlight the advantages of our proposed approach.