Optimized Gated Fusion Adaptive Graph Neural Network for Predicting Water Quality in Smart Environments

Still, an effective water prediction system is essential to support sustainable solu tions for water management in smart environments. Increased accuracy of re al-time estimates can enhance decision-making and thus lead to smaller health hazards and less damage to the environment. Therefore, the Optimized Gated Fusion Adaptive Graph Neural Network (GFAGNN) is presented in this paper for water quality prediction, which adoption of graph structures and neural networks to analyze for hitherto, unseen nonlinear interactions among water parameters. Similarly, this paper outlines the inadequacies of conventional criterion-based extrapolation models and the importance of a flexible graph-based model. The proposed GFAGNN integrates multiple sources into a comprehensive one, and it also applies the gated fusion mechanism to improve the model’s performance when operating in dynamic scenarios. To illustrate the enhancements, a compar ison between GFAGNN and a comparative model – Convolutional Neural Network (CNN) – is included in this paper. From experimental outcomes, it has been ob served that the proposed work GFAGNN outperforms the existing models in terms of accuracy and robustness in calculating water quality indices. The effectiveness of the proposed model is confirmed through multiple computations, simulation, and dataset analysis. Altogether, the results point to the effectiveness of graph neural networks concerning the development of smart water management systems.