Publisher
Springer Science and Business Media LLC
Reference56 articles.
1. Al-Sulttani, A.O.; Al-Mukhtar, M.; Roomi, A.B.; Farooque, A.A.; Khedher, K.M.; Yaseen, Z.M.: Proposition of new ensemble data-intelligence models for surface water quality prediction. IEEE Access 9, 108527–108541 (2021)
2. Yan, G.; Yu, C.; Bai, Y.: Wind turbine bearing temperature forecasting using a new data-driven ensemble approach. Machines 9(11), 248 (2021)
3. Afan, H.A.; Osman Ibrahem Ahmed, A.; Essam, Y.; Ahmed, A.N.; Huang, Y.F.; Kisi, O.; Sherif, M.; Sefelnasr, A.; Chau, K.-W.; El-Shafie, A.: Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques. Eng. Appl. Comput. Fluid Mech. 15(1), 1420–1439 (2021)
4. Wang, W.-C.; Du, Y.-J.; Chau, K.-W.; Xu, D.-M.; Liu, C.-J.; Ma, Q.: An ensemble hybrid forecasting model for annual runoff based on sample entropy, secondary decomposition, and long short-term memory neural network. Water Resour. Manage 35(14), 4695–4726 (2021)
5. Shamshirband, S.; Jafari Nodoushan, E.; Adolf, J.E.; Abdul Manaf, A.; Mosavi, A.; Chau, K.-W.: Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Eng. Appl. Comput. Fluid Mech. 13(1), 91–101 (2019)
Cited by
2 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