Open Access
| Issue |
E3S Web Conf.
Volume 638, 2025
International Conference on Electronics, Engineering Physics and Earth Science (EEPES 2025)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 8 | |
| Section | Renewable Energy and Green Technologies | |
| DOI | https://doi.org/10.1051/e3sconf/202563802006 | |
| Published online | 16 July 2025 | |
- W. L. Young, Warren L. The Box-Jenkins approach to time series analysis and forecasting: principles and applications, Rairo-operations Research, 11, pp.129–143 (1977) [Google Scholar]
- A. Gupta, A. Kumar, Two-step Daily Reservoir Inflow Prediction Using ARIMA-Machine Learning and Ensemble Models, Journal of Hydro-environment Research, 45 (2022) [Google Scholar]
- R. J. Abrahart, L. See, Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments, Hydrol Process, 14, pp. 2157–2172 (2000) [Google Scholar]
- S. K. Googhari, Y.F. Huang, A.H. B Ghazali, L. T. Shui, Neural Networks for Forecasting Daily Reservoir Inflows, Pertanika J. Sci. & Technol. 18, pp. 33–41 (2010) [Google Scholar]
- V. I. Kontopoulou, A. D. Panagopoulos, I. Kakkos, G. K. Matsopoulos, A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks, Future Internet, 15, 255 (2023) [Google Scholar]
- M. Valipour, M. E. Banihabib, S. M. R. Behbahani, Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir, Journal of Hydrology, 476, pp. 433–441 (2013) [Google Scholar]
- H. Maier, G. Dandy, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environmental Modelling & Software, 15(1), pp. 101–124 (2000) [Google Scholar]
- J. Lin, C. Cheng, K. Chau, Using support vector machines for long-term discharge prediction, Hydrology. Sci. J., 51, pp.599–612 (2006) [Google Scholar]
- Z. Wang, J. Qiu, F. Li, Hybrid models combining EMD/EEMD and ARIMA for long-term streamflow forecasting. Water, 10(7), 853 (2018) [Google Scholar]
- P. Sharma, S. Singh, S. D. Sharma, Artificial neural network approach for hydrologic river flow time series forecasting, Agr. Res 11(3):465–476 (2022) [Google Scholar]
- M. Waqas, U. W. Humphries, A critical review of RNN and LSTM variants in hydrological time series predictions, Methods X, 13, 102946 (2024) [Google Scholar]
- S. Ghimire, Z. M. Yaseen, A. A. Farooque, R. C. Deo, Ji Zhang, X. Tao, Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks, Sci Rep, NATURE 11, 17497 (2021) [Google Scholar]
- J. Singh, H. V. Knapp, M. Demissie, Hydrologic modelling of the Iroquois River watershed using HSPF and SWAT. ISWS CR 2004-08. Champaign, Ill.: Illinois State Water Survey (2004) [Google Scholar]
- https://open-meteo.com/ accessed on 24.01.2025 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.