Open Access
MATEC Web Conf.
Volume 395, 2024
2023 2nd International Conference on Physics, Computing and Mathematical (ICPCM2023)
Article Number 01005
Number of page(s) 6
Published online 15 May 2024
  1. H. Maier; A. Jain, G. Dandy, K.P. Sudheer, Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Model. Softw. 25, 891 (2010). [Google Scholar]
  2. Q. Tao, Y.U. Chuanjin. Wind speed forecasting for bridge sites based on empirical mode decomposition and Elman neural network. Journal of Catastrophology, 32, 85 (2017). [Google Scholar]
  3. V. Nourani, G. Andalib, F. Sadikoglu, E. Sharghi, Cascade-based multi-scale AI approach for modeling rainfall-runoff process. Hydrology Research, 49, 1191 (2018). [Google Scholar]
  4. D.W. Cui. Application of multi-hidden layer BP neural network models in runoff prediction. Hydrology, 33, 68 (2013). [Google Scholar]
  5. Z. Qiang, B.D. Wang, B. He, et al. Singular spectrum analysis and ARIMA hybrid model for annual runoff forecasting. Water Resources Management, 25, 2683 (2011). [Google Scholar]
  6. C.T. Cheng, W.J. Niu, Z.K. Feng, et al. Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization. Water, 7, 4232 (2015). [Google Scholar]
  7. F. Kratzert, D. Klotz, C. Brenner, et al. Rainfall-runoff modelling using Long ShortTerm Memory (LSTM) networks. Hydrology and Earth System Sciences, 22, 6005 (2018). [Google Scholar]
  8. Z. Karevan, A.K. Johan. Transductive LSTM for time-series prediction: An application to weather forecasting. Neural Networks, 125, 1 (2020). [Google Scholar]
  9. Z. Xiang, J. Yan, I. Demir. A rainfall-runoff model with LSTM-based sequence to sequence learning. Water Resources Research, 56, 1 (2020). [Google Scholar]
  10. N.S. Siami, N. Tavakoli, A.S. Namin. A Comparison of ARIMA and LSTM in Forecasting Time Series. 17th IEEE International Conference on Machine Learning and Applications (ICMLA). (IEEE, 2018). [Google Scholar]
  11. Y. Zhang, Y. Li, M. Yang, et al. Multi-scale nonlinear runoff prediction model based on EMD. International Journal of Earth Sciences and Engineering, 8, 552 (2015). [Google Scholar]
  12. N.E. Huang, Z. Shen, S.R. Long, et al. The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis. Proceedings of the Royal Society of London. 454, 903 (1998). [Google Scholar]

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