【摘要】 水质预测是水资源生态治理体系的核心环节，在时间演变、随机扰动及环境变迁等多重因素作用下，水质监测数据呈现出非平稳动态特征与多维非线性耦合关系。针对流域水质多参数预测中时空依赖复杂、传统模型难以有效融合动态拓扑与长周期特征的问题，文中提出了一种基于时空图卷积网络的水质预测算法。在时间维度上，设计掩码子序列Transformer模块，通过随机掩码自监督预训练任务使模型从长周期数据中提取趋势特征，结合扩张因果卷积捕获水质特征的累积效应，解决传统模型对突变事件的响应滞后问题。在空间维度上，构建动态图结构学习模块，融合基于站点物理距离的预定义邻接矩阵与动态残差图生成动态图结构。实验结果表明，相较于其他水质时空预测模型，该模型具有更好的预测精度，对所有水质指标的预测R~2均能达到93%以上。更多还原

【Abstract】 Accurate water quality prediction is essential for effective ecological water management. However, water quality exhibits complex non-stationary dynamics and multi-dimensional nonlinear relationships driven by temporal evolution and environmental variability. In multi-parameter river water quality prediction, intricate spatial-temporal dependencies make it difficult for traditional models to effectively integrate dynamic topology and long-period features. To address this challenge, we propose a spatial-temporal graph convolutional network(STGCN) model. In the temporal dimension, a masked sub-series transformer module is employed to extract longterm trends through self-supervised pretraining. Combined with dilated causal convolution, it captures cumulative water quality effects and alleviates the response lag common in traditional models when facing abrupt changes. In the spatial dimension, a dynamic graph learning module integrates a predefined station-distance adjacency matrix with a dynamic residual map to generate adaptive graph structures. Experimental results demonstrate that the proposed model outperforms existing methods in water quality prediction, achieving an R~2 greater than 0.93 across all water quality indicators.更多还原