Discussion of “Prediction of Bed-Load Sediment Using Newly Developed Support-Vector Machine Techniques”
This article is a reply.
VIEW THE ORIGINAL ARTICLEPublication: Journal of Irrigation and Drainage Engineering
Volume 149, Issue 9
Get full access to this article
View all available purchase options and get full access to this article.
References
Aghelpour, P., Y. Guan, H. Bahrami-Pichaghchi, B. Mohammadi, O. Kisi, and D. Zhang. 2020a. “Using the MODIS sensor for snow cover modeling and the assessment of drought effects on snow cover in a mountainous area.” Remote Sens. 12 (20): 3437. https://doi.org/10.3390/rs12203437.
Aghelpour, P., B. Mohammadi, S. M. Biazar, O. Kisi, and Z. Sourmirinezhad. 2020b. “A theoretical approach for forecasting different types of drought simultaneously, using entropy theory and machine-learning methods.” ISPRS Int. J. Geo-Inf. 9 (12): 701. https://doi.org/10.3390/ijgi9120701.
Chen, T., K. Tang, G. Chen, and X. Yao. 2012. “A large population size can be unhelpful in evolutionary algorithms.” Theor. Comput. Sci. 436 (Jun): 54–70. https://doi.org/10.1016/j.tcs.2011.02.016.
Emamgholizadeh, S., and B. Mohammadi. 2021. “New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity.” Soft Comput. 25 (21): 13451–13464. https://doi.org/10.1007/s00500-021-06095-4.
Guan, Y., B. Mohammadi, Q. B. Pham, S. Adarsh, K. S. Balkhair, K. U. Rahman, N. T. T. Linh, and D. Q. Tri. 2020. “A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model.” Theor. Appl. Climatol. 142 (1): 349–367. https://doi.org/10.1007/s00704-020-03283-4.
Hadadi, F., R. Moazenzadeh, and B. Mohammadi. 2022. “Estimation of actual evapotranspiration: A novel hybrid method based on remote sensing and artificial intelligence.” J. Hydrol. 609 (Jun): 127774. https://doi.org/10.1016/j.jhydrol.2022.127774.
Moazenzadeh, R., B. Mohammadi, Z. Duan, and M. Delghandi. 2022a. “Improving generalisation capability of artificial intelligence-based solar radiation estimator models using a bio-inspired optimisation algorithm and multi-model approach.” Environ. Sci. Pollut. Res. 29 (19): 27719–27737. https://doi.org/10.1007/s11356-021-17852-1.
Moazenzadeh, R., B. Mohammadi, M. J. S. Safari, and K. W. Chau. 2022b. “Soil moisture estimation using novel bio-inspired soft computing approaches.” Eng. Appl. Comput. Fluid Mech. 16 (1): 826–840. https://doi.org/10.1080/19942060.2022.2037467.
Mohammadi, B., R. Moazenzadeh, K. Christian, and Z. Duan. 2021. “Improving streamflow simulation by combining hydrological process-driven and artificial intelligence-based models.” Environ. Sci. Pollut. Res. 28 (46): 65752–65768. https://doi.org/10.1007/s11356-021-15563-1.
Mohammadi, B., R. Moazenzadeh, Q. B. Pham, N. Al-Ansari, K. U. Rahman, D. T. Anh, and Z. Duan. 2022a. “Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation.” Ain Shams Eng. J. 13 (1): 101498. https://doi.org/10.1016/j.asej.2021.05.012.
Mohammadi, B., M. J. S. Safari, and S. Vazifehkhah. 2022b. “IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling.” Sci. Rep. 12 (1): 1–21. https://doi.org/10.1038/s41598-022-16215-1.
Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 149 • Issue 9 • September 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 12, 2022
Accepted: Aug 23, 2022
Published online: Jun 22, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 22, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.