Prediction of flow discharge in Mahanadi River Basin, India, based on novel hybrid SVM approaches
Author:
Publisher
Springer Science and Business Media LLC
Subject
Management, Monitoring, Policy and Law,Economics and Econometrics,Geography, Planning and Development
Link
https://link.springer.com/content/pdf/10.1007/s10668-023-03412-9.pdf
Reference61 articles.
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2. Araghinejad, S., Burn, D. H., & Karamouz, M. (2006). Long‐lead probabilistic forecasting of streamflow using ocean‐atmospheric and hydrological predictors. Water Resources Research, 42.
3. Azimi, H., Bonakdari, H., Ebtehaj, I., & Michelson, D. G. (2018). A combined adaptive neuro-fuzzy inference system–firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed. Neural Computing and Applications, 29, 249–258.
4. Aziz, K., Rahman, A., Fang, G., & Shrestha, S. (2014). Application of artificial neural networks in regional flood frequency analysis: A case study for Australia. Stochastic Environmental Research and Risk Assessment, 28, 541–554.
5. Bray, M., & Han, D. (2004). Identification of support vector machines for runoff modelling. Journal of Hydroinformatics, 6, 265–280.
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