An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts.Practitioner Points Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%. This research presents a novel approach for Water Quality Index (WQI) estimation by integrating advanced optimization techniques with Deep Capsule Crystal Edge Graph Neural Networks. A standard WQI dataset undergoes preprocessing, including clean-up and the Missing Completely at Random technique. Key features are selected using the Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm and input into the neural network for prediction. The Greylag Goose Optimization Algorithm further fine-tunes the model's weights, achieving a mean squared error of 6.7 and 99% accuracy. image