Artificial intelligence-assisted water quality index determination for healthcare

Groundwater resource analysis is an important technological means of avoiding disease and controlling water pollution. In this field of study, water quality assessments are conducted using sequential parametric values collected in real time. For evaluation reasons, several state-of-the-art water quality evolution mechanism typically employs a single time-invariant model to determine the quality of Water. As a result, it is challenging to illustrate the importance of randomness and contingency in the process of water quality assessment, leading to variations and errors in the procedure of quality assessment. In consideration of these limitations, this study proposes a Digital Twin inspired Hybrid System (DTHS) for water quality assessment in real time. In addition, the degree of water quality is offered as an indication for quantitatively assessing the health risk status. Observational data from a monitoring station in Chaheru, a locality in the Phagwara district of the Indian state of Punjab, are used to demonstrate the efficacy of the proposed approach. The experimental results demonstrate the effectiveness of the proposed framework in terms of water quality determination, computational cost, and stability. The framework has achieved higher prediction accuracy (94.14%), sensitivity (93.74%), specificity (91.47%), and f-measure (92.37%), indicating its ability to accurately determine water quality. Additionally, the framework offers reduced computational delay and improved reliability and stability, making it a trustworthy solution for timely predictions with respect to water quality.