Cited by Time-varying neural networks based on local attention mechanism for time series forecasting

Projection of groundwater level fluctuations using deep learning and dynamic system response models in a drought affected area DOI

Dilip Roy,

Chitra Rani Paul,

Md. Panjarul Haque

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

Citations

Multimodal Deep Learning for Two-Year ENSO Forecast DOI

Mohammad Naisipour, Iraj Saeedpanah, Arash Adib

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 13, 2025

Language: Английский

Citations

Enhancing solar radiation forecasting accuracy with a hybrid SA-Bi-LSTM-Bi-GRU model DOI

Girijapati Sharma,

Subhash Chandra,

Arvind Yadav

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 19, 2025

Language: Английский

Citations

Middle‐ and Long‐Term Runoff Forecast Model for Water Resource and Climate Security Based on Self‐Attention Mechanism DOI

Juan Chen,

M.Y. Liu,

Weifeng Liu

et al.

Land Degradation and Development, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

ABSTRACT Reliable middle‐ and long‐term streamflow forecasts are critical for ensuring water resources management climate security. This study establishes a novel runoff forecasting model based on the self‐attention (SA) mechanism variational mode decomposition‐gated recurrent unit (VMD‐GRU) framework to improve monthly prediction accuracy. The maximal information coefficient (MIC) method is adopted screen key drivers of variability. proposed integrates VMD decompose sequence into intrinsic components applies GRU coupled with SA predict each component. whale optimization algorithm (WOA) VMD‐SA‐GRU hyperparameters, then forecast results obtained by superimposing Using 40 years data from South‐to‐North Water Diversion Project in China, evaluated against VMD‐GRU benchmarks. Results demonstrate that leverages strengths its constituent algorithms, significantly improving Compared model, enhances Nash‐Sutcliff efficiency (NSE) 6%–35%, reduces root mean square error (RMSE) 15%–30%, decreases absolute (MAE) 15%–33%. robust provides reliable tool sustainable resource addressing climate‐related challenges.

Language: Английский

Citations

Wavelet and AI-based reservoir evaporation modeling for optimized water management: a case study of Koudiat Acerdoun Dam DOI

Leila Benchaiba,

Abderzak Moussouni, Amer Zeghmar

et al.

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(4)

Published: March 28, 2025

Language: Английский

Citations

Time-varying neural networks based on local attention mechanism for time series forecasting DOI

Yuxuan Wang, Hongbo Xiao, Xiao Jian-hua

et al.

Published: Dec. 20, 2024

Language: Английский

Citations