Smart Aqua 4.0: A Cyber-Intelligent System for Predictive Water Management with AI

Aim: The aim of the research is to create Smart Aqua 4.0, an AI-powered water resource management system that uses predictive analytics and Heuristic algorithm to determine whether water is potable or not based on important quality factors. Materials and Methods: In this study, Two methods were evaluated: Group 1, which applies a Heuristic Algorithm with preset threshold rules based on WHO criteria, and Group 2, which uses a Predictive Analytics Model to analyze water quality parameters (conductivity, pH, turbidity, etc.) through feature scaling and cross-validation. A 93% confidence level was used to determine statistical significance for the analysis of 2,500 water samples. Results: Group 1, which employed the Predictive Analytics Model, outperformed Group 2, which employed the Heuristic Algorithm, by a significant margin, achieving an accuracy of 92.47%. When comparing the two methods, the performance difference was statistically significant (p < 0.05), suggesting that the predictive model was more successful in classifying the quality of the water. Conclusion: Water quality classification was made more accurate and dependable by the Predictive Analytics Model than by the heuristic approach, especially in regions with limited resources.