Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad-Reference-Cited by-同舟云学术

Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad

Published:2019-10-25 Issue:11 Volume:191 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Al-Mukhtar Mustafa^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s10661-019-7821-5.pdf

Reference69 articles.

1. Afan, H. A., El-shafie, A., Mohtar, W. H. M. W., & Yaseen, Z. M. (2016). Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction. Journal of Hydrology, 541, 902–913.

2. Al-Ansari, N., Ali, S., & Taqa, A. (1979). Sediment discharge of the River Tigris at Baghdad (Iraq). Canberra Symposium: The Hydrology of Areas of Low Precipitation, (July).

3. Ali, A. A., Al-Ansari, N. A., Al-suhail, Q., & Knutsson, S. (2017). Spatial measurement of bed load transport in Tigris River. Journal of Earth Sciences and Geotechnical Engineering, 7(4), 55–75.

4. Al-Mukhtar, M. (2016). Modelling the root zone soil moisture using artificial neural networks, a case study. Environmental Earth Sciences, 75(15), 1124.

5. Al-Mukhtar, M., & Al-Yaseen, F. (2019). Modeling water quality parameters using data-driven models, a case study Abu-Ziriq Marsh in South of Iraq. Hydrology, 6(1), 24.

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