Application of a hybrid deep learning approach with attention mechanism for evapotranspiration prediction: a case study from the Mount Tai region, China-Reference-Cited by-同舟云学术

Application of a hybrid deep learning approach with attention mechanism for evapotranspiration prediction: a case study from the Mount Tai region, China

Published:2023-09-11 Issue:4 Volume:16 Page:3469-3487
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Wang Shichao,Yu Xiaoge,Li Yan,Wang Shujun,Meng Can

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-023-01103-7.pdf

Reference39 articles.

1. Abed M, Imteaz MA, Ahmed AN, Huang YF (2022) Modelling monthly pan evapotranspiration utilising Random Forest and deep learning algorithms. Sci Rep-Uk 12(1):13132. https://doi.org/10.1038/s41598-022-17263-3

2. Allawi MF, El-Shafie A (2016) Utilizing RBF-NN and ANFIS methods for multi-lead ahead prediction model of evapotranspiration from reservoir. Water Resour Manag 30:4773–4788. https://doi.org/10.1007/s11269-016-1452-1

3. Allawi MF, Binti Othman F, Afan HA, Ahmed AN, Hossain MS, Fai CM, El-Shafie A (2019) Reservoir evapotranspiration prediction modeling based on artificial intelligence methods. Water-Sui 11(6):1226. https://doi.org/10.3390/w11061226

4. Allawi MF, Ahmed ML, Aidan IA, Deo RC, El-Shafie A (2021) Developing reservoir evapotranspiration predictive model for successful dam management. Stoch Env Res Risk A 35:499–514

5. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Farhan L (2021) Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:1–74. https://doi.org/10.1186/s40537-021-00444-8

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Future Reference Evapotranspiration Trends in Shandong Province, China: Based on SAO-CNN-BiGRU-Attention and CMIP6;Agriculture;2024-09-09

2. A gap filling method for daily evapotranspiration of global flux data sets based on deep learning;Journal of Hydrology;2024-09

3. Application of wavelet-based multivariate long short-term memory models in prediction of stage for Teesta River, India;Journal of Hydroinformatics;2024-08-01

4. Adaptive Hybrid Potential Evapotranspiration (Pet) Prediction Method Based on Automatic Machine Learning;2024