Work

Spatiotemporal Modeling of Surface Water–Groundwater Interactions via Multi-Task Transformer-Based Learning

Year: 2025

Type: article

Abstract: A spatiotemporal, multi-task learning (MTL) model for simulating surface water–groundwater (SW-GW) dynamics is developed and applied to the Heihe River Basin, Northwest China. The Transformer-based model (MT-TFT) jointly forecasts surface runoff and groundwater levels, outperforming MTL models built on gated recurrent unit (GRU) and long short-term memory (LSTM) architectures. Compared with single-task learning, adding a coupled groundwater-level task markedly improves surface runoff prediction, achieving a Nash–Sutcliffe efficiency (NSE) of 0.73 and a coefficient of determination (R2) of 0.75. Attention-based interpretability shows that the model assigns the highest weights to time steps with elevated precipitation; as lead time shortens, attention further concentrates on these periods, improving the accuracy of near-term, multi-step forecasts. These results highlight the value of inductive transfer across hydrologic targets and demonstrate that MT-TFT provides an effective, interpretable framework for SW–GW coupling.

Source: Hydrology

DOI: 10.3390/hydrology12110291

Authors: Hao Jing, Yong Tian, Chunmiao Zheng

Institutions: Harbin Institute of Technology, Southern University of Science and Technology, Eastern Institute of Technology, Ningbo

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $2,172