A Survey on Hybrid Models Used for Hydrological Time-Series Forecasting
Author:
Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-15-7533-4_19
Reference23 articles.
1. Mahalakshmi G, Sridevi S, Rajaram S (2016) A survey on forecasting of time series data. In: 2016 international conference computing technologies and intelligent data engineering, pp 1–8. IEEE. https://doi.org/10.1109/icctide.2016.7725358
2. Nazir HM, Hussain I, Faisal M, Shoukry AM, Gani S, Ahmad I (2019) Development of multidecomposition hybrid model for hydrological time series analysis. Complexity 1–14
3. Huang G, Wang L (2011) Hybrid neural network models for hydrologic time series forecasting. In: 2011 fourth international joint conference on computational sciences and optimization, pp 1347–1350. IEEE. https://doi.org/10.1109/cso.2011.147
4. Gjika E, Ferrja A, Kamberi A (2019) A study on the efficiency of hybrid models in forecasting precipitations and water inflow Albania case study. Adv Sci Technol Eng Syst J (ASTESJ) 4(1):302–310
5. Di C, Yang X, Wang X (2014) A four-stage hybrid model for hydrological time series forecasting. PLoS ONE 9(8):1–18. https://doi.org/10.1371/journal.pone.0104663
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