Closure to “Performance Enhancement of a Conceptual Hydrological Model by Integrating Artificial Intelligence” by Ahmet Ali Kumanlioglu and Okan Fistikoglu
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VIEW THE ORIGINAL ARTICLEAuthors: Ahmet Kumanlioglu https://orcid.org/0000-0001-7073-0322 [email protected] and Okan Fistikoglu https://orcid.org/0000-0002-9483-1563 [email protected]Author Affiliations
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 9
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References
Anctil, F., C. Michel, C. Perrin, and V. Andreassian. 2004. “A soil moisture index as an auxiliary ANN input for stream flow forecasting.” J. Hydrol. 286 (1–4): 155–167. https://doi.org/10.1016/j.jhydrol.2003.09.006.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000a. “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrol. Eng. 5 (2): 115–123. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(115).
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000b. “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrol. Eng. 5 (2): 124–137. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124).
Goldberg, D. E. 1989. Genetic algorithm in search optimization and machine learning. Boston: Addison-Wesley Longman.
Tayfur, G. 2012. Soft computing in water resources engineering. Southampton, UK: WIT Press.
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Journal of Hydrologic Engineering
Volume 25 • Issue 9 • September 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Apr 7, 2020
Accepted: Apr 28, 2020
Published online: Jun 29, 2020
Published in print: Sep 1, 2020
Discussion open until: Nov 29, 2020
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Authors
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Assistant Professor, Dept. of Civil Engineering, Manisa Celal Bayar Univ., 45140 Manisa, Turkey (corresponding author). ORCID: https://orcid.org/0000-0001-7073-0322. Email: [email protected]
Associate Professor, Dept. of Civil Engineering, Dokuz Eylul Univ., 35160 İzmir, Turkey. ORCID: https://orcid.org/0000-0002-9483-1563. Email: [email protected]
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