Suspended sediment load prediction using artificial intelligence techniques: comparison between four state-of-the-art artificial neural network techniques-Reference-Cited by-同舟云学术

Suspended sediment load prediction using artificial intelligence techniques: comparison between four state-of-the-art artificial neural network techniques

Published:2021-02 Issue:3 Volume:14 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Rezaei Khalil^ORCID,Pradhan Biswajeet,Vadiati Meysam,Nadiri Ata Allah

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

http://link.springer.com/content/pdf/10.1007/s12517-020-06408-1.pdf

Reference102 articles.

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2. Ahn KH, Yellen B, Steinschneider S (2017) Dynamic linear models to explore time-varying suspended sediment-discharge rating curves. Water Resour Res 53:4802–4820. https://doi.org/10.1002/2017WR020381

3. Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model Softw 22:2–13

4. Altun H, Bilgil A, Fidan BC (2007) Treatment of multi-dimensional data to enhance neural network estimators in regression problems. Expert Syst Appl 32:599–605

5. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000a) Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng 5:115–123

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