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

и другие.

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 19, 2024

Язык: Английский

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

и другие.

Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 59, С. 102382 - 102382

Опубликована: Апрель 17, 2025

Язык: Английский

Процитировано

1
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

и другие.

Journal of Hydrology, Год журнала: 2024, Номер 650, С. 132496 - 132496

Опубликована: Дек. 16, 2024

Язык: Английский

Процитировано

5
Research on data augmentation and synthetic sample quantity uncertainty in few-shot wind power prediction based on the adaptive CRITIC-HLICRVFL method DOI

Shihao Song,

Anbo Meng, Liexi Xiao

и другие.

Renewable Energy, Год журнала: 2025, Номер unknown, С. 123527 - 123527

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Pixel-based agricultural drought forecasting based on deep learning approach: Considering the linear trend and residual feature of vegetation temperature condition index DOI
Fengwei Guo, Pengxin Wang, Kevin Tansey

и другие.

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 237, С. 110570 - 110570

Опубликована: Май 21, 2025

Язык: Английский

Процитировано

0
Dcswinlstm: A Fusion Method on Multiscale Spatiotemporal Meteorological Drought Prediction DOI
H.Y. Peng,

Chengrong Wu,

Yuhao Du

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Artificial-Intelligence-Based Model for Early Strong Wind Warnings for High-Speed Railway System DOI Open Access
Wei Gu, Hongyan Xing, Guoyuan Yang

и другие.

Electronics, Год журнала: 2024, Номер 13(23), С. 4582 - 4582

Опубликована: Ноя. 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.

Язык: Английский

Процитировано

0
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

и другие.

Earth Science Informatics, Год журнала: 2024, Номер 18(1)

Опубликована: Дек. 19, 2024

Язык: Английский

Процитировано

0