Cited by Development of deep learning approaches for drought forecasting: a comparative study in a cold and semi-arid region

Enhancing multi-temporal drought forecasting accuracy for Iran: Integrating an innovative hidden pattern identifier, recursive feature elimination, and explainable ensemble learning DOI

Mahnoosh Moghaddasi,

Mansour Moradi,

Mehdi Mohammadi Ghaleni

et al.

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102382 - 102382

Published: April 17, 2025

Language: Английский

Citations

Improved random vector functional link network with an enhanced remora optimization algorithm for predicting monthly streamflow DOI

Rana Muhammad Adnan,

Reham R. Mostafa, Mo Wang

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 650, P. 132496 - 132496

Published: Dec. 16, 2024

Language: Английский

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Artificial-Intelligence-Based Model for Early Strong Wind Warnings for High-Speed Railway System DOI

Wei Gu, Hongyan Xing, Guoyuan Yang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(23), P. 4582 - 4582

Published: Nov. 21, 2024

Wind speed prediction (WSP) provides future wind information and is crucial for ensuring the safety of high-speed railway systems (HSRs). However, accurate (WS) remains a challenge due to nonstationary nonlinearity patterns. To address this issue, novel artificial-intelligence-based WSP model (EE-VMD-TCGRU) proposed in paper. EE-VMD-TCGRU combines energy-entropy-guided variational mode decomposition (EE-VMD) with customized hybrid network, TCGRU, that incorporates loss function: Gaussian kernel mean square error (GMSE). Initially, raw WS sequence decomposed into various frequency-band components using EE-VMD. TCGRU then applied each component capture both long-term trends short-term fluctuations. Furthermore, function, GMSE, introduced training analyze WS’s nonlinear patterns improve accuracy. Experiments conducted on real-world data from Beijing–Baotou demonstrate outperforms benchmark models, achieving absolute (MAE) 0.4986, (MSE) 0.4962, root (RMSE) 0.7044, coefficient determination (R2) 94.58%. These results prove efficacy train operation under strong environments.

Language: Английский

Citations

Development of deep learning approaches for drought forecasting: a comparative study in a cold and semi-arid region DOI

Amin Gharehbaghi, Redvan Ghasemlounıa, Babak Vaheddoost

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 19, 2024

Language: Английский

Citations