University Of Tehran Press Pollution 2383-451X 11 1 2025 01 01 Fuzzy Synthetic Evaluation for classifying Groundwater quality for irrigation in the parts of Tumkur district, Karnataka: Insights of uncertainty management at class boundary condition 1 14 98781 10.22059/poll.2024.373995.2285 EN Mahamadhussen Thabrez School of Civil Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India 0009-0008-4138-2988 Sundaram Parimalarenganayaki School of Civil Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India 0000-0001-7174-630X Journal Article 2024 03 15 Sustainability of irrigated agriculture is based on the efficient management of quantity and quality of water resources. Water Quality Indices used to assess the suitability of irrigation water, however, often consist of uncertainties arise near the class boundaries. Hence, the objective of the present study is to classify the groundwater for irrigation purpose in Tumkur district, Karnataka, India, using Fuzzy comprehensive evaluation approach for crisp classification. The methodology of this study includes collection of 104 groundwater samples, assessment of hydrogeochemistry, and classification of groundwater by conventional and Fuzzy-logic techniques. Hydrogeochemistry by Piper plot indicates mixed Na-Ca-HCO3 type and Gibbs plot indicates the influence of rock-water interactions. The water classification by conventional irrigation indices such as Electrical Conductivity, Sodium Absorption Ratio, Kelly Index, Percentage Sodium, Residual Sodium Carbonate and Magnesium Hazard showed that 2%, 0%, 86.5%, 40%, 25% (post monsoon) and 4%, 2%, 81%, 38.5%, 4% and 19.2% (pre-monsoon) of groundwater samples were not suitable, respectively. As various indices indicated dissimilar results, an integrated conventional index was evaluated by Fuzzy synthetic evaluation technique based on the Maximum Principle Membership and Fuzzy Class Ratio (FCR) and it showed 3.8 % and 0.98% of samples were classified as Not suitable (N), respectively. However, FCR method was found to be effective in dealing variation in fuzzy boundary conditions and it showed 0.98%, 1.96%, 1.96%, 1.96% samples as not suitable at 5%, 10%, 15% and 20% of degree of variation near class boundaries, respectively.  https://jpoll.ut.ac.ir/article_98781_1a47daf9eda54fb88c36fd23e6943915.pdf