TeaNet: An Enhanced Attention Network for Climate-Resilient River Discharge Forecasting Under CMIP6 SSP585 Projections

Prashant Parasar (), Poonam Moral, Aman Srivastava, Akhouri Pramod Krishna, Richa Sharma, Virendra Singh Rathore, Abhijit Mustafi, Arun Pratap Mishra, Fahdah Falah Ben Hasher and Mohamed Zhran ()
Additional contact information
Prashant Parasar: Department of Remote Sensing, Birla Institute of Technology, Ranchi 835215, India
Poonam Moral: Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi 835215, India
Aman Srivastava: Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur 721302, India
Akhouri Pramod Krishna: Department of Remote Sensing, Birla Institute of Technology, Ranchi 835215, India
Richa Sharma: Department of Remote Sensing, Birla Institute of Technology, Ranchi 835215, India
Virendra Singh Rathore: Department of Remote Sensing, Birla Institute of Technology, Ranchi 835215, India
Abhijit Mustafi: Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi 835215, India
Arun Pratap Mishra: Department of Forestry and Remote Sensing, Earthtree Enviro Private Ltd., Shillong 793012, India
Fahdah Falah Ben Hasher: Department of Geography and Environmental Sustainability, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mohamed Zhran: Public Works Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt

Sustainability, 2025, vol. 17, issue 9, 1-28

Abstract: The accurate prediction of river discharge is essential in water resource management, particularly under variability due to climate change. Traditional hydrological models commonly struggle to capture the complex, nonlinear relationships between climate variables and river discharge, leading to uncertainties in long-term projections. To mitigate these challenges, this research integrates machine learning (ML) and deep learning (DL) techniques to predict discharge in the Subernarekha River Basin (India) under future climate scenarios. Global climate models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) are assessed for their ability to reproduce historical discharge trends. The selected CNRM-M6-1 model is bias-corrected and downscaled before being used to simulate future discharge patterns under SSP585 (a high-emission scenario). Various AI-driven models, such as a temporal convolutional network (TCN), a gated recurrent unit (GRU), a support vector regressor (SVR), and a novel DL network named the Temporal Enhanced Attention Network (TeaNet), are implemented by integrating the maximum and minimum daily temperatures and precipitation as key input parameters. The performance of the models is evaluated using the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R 2 ). Among the evaluated models, TeaNet demonstrates the best performance, with the lowest error rates (RMSE: 2.34–3.04; MAE: 1.13–1.52 during training) and highest R 2 (0.87–0.95), outperforming the TCN (R 2 : 0.79–0.88), GRU (R 2 : 0.75–0.84), SVR (R 2 : 0.68–0.80), and RF (R 2 : 0.72–0.82) by 8–15% in accuracy across four gauge stations. The efficacy of the proposed model lies in its enhanced attention mechanism, which successfully identifies temporal relationships in hydrological information. In determining the most relevant predictors of river discharge, the feature importance is analyzed using the proposed TeaNet model. The findings of this research strengthen the role of DL architectures in improving long-term discharge prediction, providing valuable knowledge for climate adaptation and strategic planning in the Subernarekha region.

Keywords: CMIP6; climate change; forecasting; GCM; machine learning; river discharge; temporal convolutional network (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/2071-1050/17/9/4230/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/9/4230/ (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:9:p:4230-:d:1650837

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 ().