Graph Neural Network Implementation for Spatial-Temporal Prediction of Environmental Parameters Using Distributed Electrochemical Sensor Data

Good agreement in the spatial and temporal prediction of environmental parameters is essential for early warning systems, regulatory compliance, and adaptive control of water and atmospheric treatment processes. Non-stationarity and non-uniform sampling in distributed sensor networks, especially with cheap electrochemical sensors that produce noisy, drifting, and incomplete data, are not well represented by traditional time-series and spatial-interpolation techniques. Although Spatio-temporal Graph Neural Networks (ST-GNNs) have demonstrated their effectiveness in environmental forecasting, current methods rely on sparsely spaced monitoring stations and coarse-resolution sensors and do not account for electrochemical sensing properties. The article presents the Spatial-Temporal Environmental Graph Neural Network (ST-EnvGNN), an algorithm for predicting environmental parameters from distributed electrochemical sensor measurements. ST-EnvGNN is a dynamic multi-relational graph coded by physical distance, hydraulic connectivity and similarity between sensors based on correlation. The spatial-temporal encoder uses an adaptive attention mechanism and graph convolution with gated recurrent units to learn local diffusion patterns and temporal dynamics, which are time-dependent informative sensor nodes. The framework uses masked graph propagation and sturdy loss functions to address the noise and missing data. We also introduce an Uncertainty-Aware Prediction Head (UAPH) that predicts probability distributions and can make point predictions to support risk-averse decision-making. Real-world environmental monitoring experiments indicate that ST-EnvGNN reduces Root Mean Square Error (RMSE) by 45.7 per cent relative to persistence and by 10.7 per cent relative to the optimal graph neural network base, while producing calibrated uncertainty estimates. The contribution of each proposed component is confirmed by ablation studies.