A New Multi-Criteria Decision-Making Model for River Pollution Assessment Using Hesitant Fuzzy Soft Multisets: A Case Study of the Haora River, Tripura (Northeast India)

Resumo

This study introduces a new way to assess river water pollution using a method called the hesitant fuzzy soft multiset (Ή𝔉SΜS) model. It introduces new concepts such as the 𝐿𝐸𝑉𝐸𝐿-Ή𝔉SΜS, root mean square operator (RΜSО), and root mean square matrix (RΜSΜ) to build a reliable multi-criteria decision-making (ϺϹDϺ) model. To show how this method works, we applied it to evaluate the water pollution levels in the Haora River in Tripura, India. The Haora River is very important for the people living in Tripura. It supplies drinking water to Agartala city and supports fishing, farming, and religious activities. Many people depend on the river for their daily needs. However, pollution from household waste and farming is harming the river. We used 14 important water quality parameters (𝒲𝒬𝒫𝓈), including pH, Total Dissolved Solids, Dissolved Oxygen, Electrical Conductivity, Calcium, Magnesium, Total Alkalinity, Total Hardness, Chloride, Total Coliform, Fecal Coliform, and Biochemical Oxygen Demand, tested at seven different locations during three seasons between April 2022 and March 2023. Our model uses a scoring method called the λ-𝐿𝐸𝑉𝐸𝐿-score to assess the overall water quality. The results showed that this method is effective in dealing with uncertain data and helps in making better decisions about managing river pollution. Overall, the study shows that the Ή𝔉SΜS-based model is a helpful tool for evaluating water quality in complex real-life situations.