Funder
open fund of the Key Laboratory of Sediment Science and Northern River Training, the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research
Publisher
Springer Science and Business Media LLC
Reference74 articles.
1. Ahmad T, Zhang D (2022) A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting. Energy 239:122109
2. Aydin HE, Iban MC (2023) Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley additive exPlanations. Nat Hazards 116(3):2957–2991
3. Aye G, Gupta R, Hammoudeh S, Kim WJ (2015) Forecasting the price of gold using dynamic model averaging. Int Rev Financ Anal 41:257–266
4. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. http://arxiv.org/abs/1803.01271
5. Bakhshali A, Najafi H, Hamgini BB, Zhang Z (2023) Neural network architectures for optical channel nonlinear compensation in digital subcarrier multiplexing systems. Opt Express 31(16):26418–26434