An Effective Framework for Improving Performance of Daily Streamflow Estimation Using Statistical Methods Coupled with Artificial Neural Network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geochemistry and Petrology,Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s00024-023-03344-5.pdf
Reference39 articles.
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2. Asquith, W. H., Roussel, M. C. & Vrabel, J. (2006). Statewide analysis of the drainage-area ratio method for 34 streamflow percentile ranges in Texas. US Geological Survey. Scientific Investigations Report 2006–5286.
3. Besaw, L. E., Rizzo, D. M., Bierman, P. R., & Hackett, W. R. (2010). Advances in ungauged streamflow prediction using artificial neural networks. Journal of Hydrology, 386(1–4), 27–37. https://doi.org/10.1016/j.jhydrol.2010.02.037
4. Burgan, H. I., & Aksoy, H. (2022). Daily flow duration curve model for ungauged intermittent subbasins of gauged rivers. Journal of Hydrology, 604, 127249. https://doi.org/10.1016/j.jhydrol.2021.127249
5. Emerson, D. G., Vecchia, A. V. & Dahl, A. L. (2005). Evaluation of drainage-area ratio method used to estimate streamflow for the Red River of the North Basin, North Dakota and Minnesota. US Geological Survey. Scientific Investigations Report 2005–5017.
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