Accurate forecasting of urban rainfall-runoff pollution across large river basins is essential for urban water management. However, this task faces formidable challenges due to the scarcity of locally monitored data and the heterogeneity in hydrological and pollution processes. To address these challenges, we proposed a novel three-tiered framework comprising (1) functional area clustering using 16-dimensional features to identify zones with shared pollution mechanisms and establish a physical parameter library; (2) a hybrid physics-informed data-driven model integrating SWMM with a Residual-BiLSTM-Multi-Head Attention (RLA) model; and (3) cluster-based transfer learning enabling predictions in data-scarce zones. The framework’s efficacy was demonstrated through a multi-tiered dataset for the Yangtze River Basin. First, a knowledge base comprising 2390 reported rainfall events across 57 functional areas was synthesized to inform the functional clustering and establish a shared physical parameter library. Subsequently, intensive field monitoring from two representative residential areas was used to train and validate the hybrid model. In data-rich zones within a cluster, the model achieved high accuracy (R2 > 0.82). For data-scarce zones within the same functional cluster, the model maintained a promising performance (R2 > 0.5). This study presents a novel basin-scale framework, with its initial application and preliminary validation in the Yangtze River Basin.
Sun et al. (Sat,) studied this question.
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