Work

A Hybrid XAJ-LSTM-TFM Model for Improved Runoff Simulation in the Poyang Lake Basin: Integrating Physical Processes with Temporal and Lag Feature Learning

Year: 2025

Type: article

Abstract: As the largest freshwater lake in China, Poyang Lake plays a crucial role in hydrological processes. Conventional models often fail to capture the time-lagged relationships between meteorological drivers and runoff responses, while lacking regional generalization capability. To address these limitations, this study proposes a novel XAJ-LSTM-TFM hybrid model that accounts for time-lagged hydrological responses and enhances the regional applicability of the Xinanjiang model. The model innovatively integrates the physical mechanisms of the Xinanjiang model with the temporal learning capacity of LSTM networks. By incorporating intermediate hydrological variables (including interflow and groundwater flow) along with 1–3 day lagged meteorological features, the model achieves an average 15.3% improvement in Nash–Sutcliffe Efficiency (NSE) across five sub-basins, with the Ganjiang Basin attaining an NSE of 0.812 and a 25.7% reduction in flood peak errors. The results demonstrate superior runoff simulation performance and reliable generalization capability under intensive anthropogenic activities.

Source: Water

DOI: 10.3390/w17142146

Authors: Haoyu Jiang, Chunxiao Zhang

Institution: China University of Geosciences (Beijing)

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Climate action

Open Access status: gold

Estimated APC paid: $3,137