Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows-Reference-Cited by-同舟云学术

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Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows

Published:2018-07-26 Issue:13 Volume:32 Page:4201-4215
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Ghorbani M. A.,Khatibi R.,Karimi V.,Yaseen Zaher Mundher,Zounemat-Kermani M.

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

http://link.springer.com/article/10.1007/s11269-018-2038-x/fulltext.html

Reference29 articles.

1. Al-Shammari ET, Mohammadi K, Keivani A et al (2016) Prediction of daily dewpoint temperature using a model combining the support vector machine with firefly algorithm. J Irrig Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001015

2. Ch S, Anand N, Panigrahi BK, Mathur S (2013) Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23. https://doi.org/10.1016/j.neucom.2012.07.017

3. Ch S, Sohani SK, Kumar D, et al (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288. https://doi.org/10.1016/j.neucom.2013.09.030

4. Clemen RT (1989) Combining forecasts: a review and annotated bibliography. Int J Forecast 5(4):559–583

5. Collobert R, Williamson RC (2001) SVM torch: support vector Machines for Large-Scale Regression Problems. J Mach Learn Res 1:143–160. https://doi.org/10.1162/15324430152733142

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