A systematic investigation of multi-attributive border approximation area comparison approach with Gaussian membership function for optimizing water quality

Ensuring the safety of drinking water is of utmost importance for the well-being of the general population. This study presents a new framework to assist specialists in selecting and assessing water samples, dealing with the urgent requirement for accurate and consistent analysis of water quality. We utilize the Gaussian Membership Function (GMF) to handle the inherent uncertainty in a dataset of 3276 samples obtained from Kaggle. The criteria importance through the inter-criteria correlation (CRITIC) technique is employed to evaluate and rank the significance of several criteria. We use the weighted aggregate sum product assessment (WASPAS) and multi-attributive border approximation area comparison (MABAC) methods to rank the water samples. Each technique has a comprehensive pseudocode to support it, which offers clear instructions for putting it into practice on various datasets. A comparative analysis confirms the efficacy and computational efficiency of these methods, showcasing their ability to improve assessments of water quality greatly. This research not only creates a strong and practical foundation for decision-making in water quality analysis but also adds to the progress of techniques. The acquired insights provide useful guidance for future research, potentially impacting the establishment of optimal methods in the evaluation of water quality.