Groundwater Level Prediction Using a Hybrid TCN–Transformer–LSTM Model and Multi-Source Data Fusion: A Case Study of the Kuitun River Basin, Xinjiang

Yankun Liu, Mingliang Du (), Xiaofei Ma, Shuting Hu and Ziyun Tuo
Additional contact information
Yankun Liu: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Mingliang Du: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Xiaofei Ma: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Shuting Hu: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Ziyun Tuo: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Sustainability, 2025, vol. 17, issue 19, 1-17

Abstract: Groundwater level (GWL) prediction in arid regions faces two fundamental challenges in conventional numerical modeling: (i) irreducible parameter uncertainty, which systematically reduces predictive accuracy; (ii) oversimplification of nonlinear process interactions, which leads to error propagation. Although machine learning (ML) methods demonstrate strong nonlinear mapping capabilities, their standalone applications often encounter prediction bias and face the accuracy–generalization trade-off. This study proposes a hybrid TCN–Transformer–LSTM (TTL) model designed to address three key challenges in groundwater prediction: high-frequency fluctuations, medium-range dependencies, and long-term memory effects. The TTL framework integrates TCN layers for short-term features, Transformer blocks to model cross-temporal dependencies, and LSTM to preserve long-term memory, with residual connections facilitating hierarchical feature fusion. The results indicate that (1) at the monthly scale, TTL reduced RMSE by 20.7% ( p < 0.01) and increased R 2 by 0.15 compared with the Groundwater Modeling System (GMS); (2) during abrupt hydrological events, TTL achieved superior performance (R 2 = 0.96–0.98, MAE < 0.6 m); (3) PCA revealed site-specific responses, corroborating the adaptability and interpretability of TTL; (4) Grad-CAM analysis demonstrated that the model captures physically interpretable attention mechanisms—particularly evapotranspiration and rainfall—thereby providing clear cause–effect explanations and enhancing transparency beyond black-box models. This transferable framework supports groundwater forecasting, risk warning, and practical deployment in arid regions, thereby contributing to sustainable water resource management.

Keywords: TCN–Transformer–LSTM; hybrid deep learning; GWL prediction; numerical simulation comparison; principal component analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/19/8544/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/19/8544/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:19:p:8544-:d:1756484

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().