The prediction of future water quality, which involves leveraging historical water quality data from various observation nodes and their corresponding topological relationships, is recognized as a critical application of graph neural networks in environmental protection. This task is complicated by the presence of noise within both the collected numerical data and the inter-node topological structures, compounded by a coupling phenomenon. The varying directions of pollutant flow intensify the complexity of coupling between numerical and structural noise. To address these challenges, a novel tendency-aware graph neural network is proposed for water quality prediction with coupled noise. First, historical water quality trend features are used to uncover local interdependencies among raw water quality indicators, enabling the construction of multiple potential hydrological topological structures and the disentanglement of structural noise. Second, spatio-temporal features are extracted from the constructed adjacency matrices and original data to separate numerical noise. Finally, water quality predictions are obtained by aggregating coherent node representations derived from the inferred latent structures across pre- and post-structure construction phases. Experimental results demonstrate that the proposed method outperforms state-of-the-art models on real-world datasets and generates potential hydrological topological structures that closely align with actual observations. The code and data are publicly available on GitHub: https://github.com/aTongs1/TaGNN.