Urban Runoff Pollution Forecasting in the Yangtze River Basin: A Physics-Informed Data-Driven Framework Enhanced with Cluster-Based Transfer Learning
Year: 2026
Type: article
Abstract: 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.
Source: Water
DOI: 10.3390/w18091095
Authors: Yacheng Sun, Yasong Chen, Yuzhen Li, Tingting Li, Wenlong Zhang
Institutions: Hohai University, China Three Gorges Corporation (China)
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7160061145