A Symmetry-Regularized Fuzzy Graph Learning Framework for Spatio-Temporal Water Quality Prediction

Water quality prediction is essential for sustainable environmental management, yet many existing machine learning models remain limited by weak interpretability, insufficient spatio-temporal representation, and reduced robustness under noisy or incomplete sensor measurements. Recent studies have also emphasized the need for adaptive, explainable, and real-time water-quality monitoring systems. In this work, we propose a symmetry-regularized fuzzy graph learning framework for forecasting and classifying water quality. The proposed approach represents monitoring stations as graph nodes, while hydrological, geographical, or similarity-based relationships define the graph edges. Standard physicochemical variables, including pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, total suspended solids, and ammoniacal nitrogen, are used as model inputs. The central idea is to incorporate symmetry principles into the learning process, so that predictions remain consistent under sensor reordering, spatially similar station behavior, seasonal cyclic patterns, and small perturbations in correlated water-quality variables. A fuzzy classification layer is further introduced to handle uncertainty near water-quality class boundaries. The proposed framework is designed to predict both continuous water quality index values and categorical pollution classes. This study provides a new symmetry-guided direction for intelligent water-quality monitoring by integrating graph learning, fuzzy reasoning, and symmetry-consistency regularization. The proposed model has potential to improve robustness, transparency, and decision support in environmental water management.