Performance evaluation of ML techniques in hydrologic studies: Comparing streamflow simulated by SWAT, GR4J, and state-of-the-art ML-based models
Author:
Funder
World Bank Group
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12040-024-02340-0.pdf
Reference42 articles.
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2. Amir Md S I I, Khan M M K, Rasul M G, Sharma R H and Akram F 2013 Automatic multi-objective calibration of a rainfall runoff model for the Fitzroy Basin, Queensland, Australia; Int. J. Environ. Sci. Dev. 4(3) 311–315, https://doi.org/10.7763/ijesd.2013.v4.361.
3. Arnold J G, Srinivasan R, Muttiah R S, and Williams J R 1998 Large area hydrologic modeling and assessment. Part I: Model development; J. Am. Water Resour. Assoc. 34(1) 73–89, https://doi.org/10.1111/j.1752-1688.1998.tb05961.x.
4. Barbhuiya S, Raghuvanshi A S and Tiwari H L 2023 Assessment of streamflow in ungauged basin by using physical similarity approach; Arab. J. Geosci. 16(12) 672, https://doi.org/10.1007/s12517-023-11786-3.
5. Behzad M, Asghari K, Eazi M and Palhang M 2009 Generalization performance of support vector machines and neural networks in runoff modeling; Expert Syst. Appl. 36(4) 7624–7629, https://doi.org/10.1016/j.eswa.2008.09.053.
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