A brief review of recent global trends in suspended sediment estimation studies
Author:
Affiliation:
1. Graduate School of Symbiotic Science and Technology, Fukushima University, Japan
2. LINKAGE Project, Research Institute for Humanity and Nature, Japan
Publisher
Japan Society of Hydrology and Water Resources
Link
https://www.jstage.jst.go.jp/article/hrl/18/2/18_51/_pdf
Reference61 articles.
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2. Adnan RM, Yaseen ZM, Heddam S, Shahid S, Sadeghi-Niaraki A, Kisi O. 2022. Predictability performance enhancement for suspended sediment in rivers: inspection of newly developed hybrid adaptive neuro-fuzzy system model. International Journal of Sediment Research 37: 383–398. DOI: 10.1016/j.ijsrc.2021.10.001.
3. Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Wan Mohtar WHM, Hussain A. 2015. ANN based sediment prediction model utilizing different input scenarios. Water Resources Management 29: 1231–1245. DOI: 10.1007/s11269-014-0870-1.
4. AlDahoul N, Essam Y, Kumar P, Ahmed AN, Sherif M, Sefelnasr A, Elshafie A. 2021. Suspended sediment load prediction using long short-term memory neural network. Scientific Reports 11: 7826. DOI: 10.1038/s41598-021-87415-4.
5. Ali HL, Mohammed TA, Yusuf BB, Aziz AA. 2018. A simplification of the Einstein equation for the calculation of suspended sediment transport in rivers. International Journal of Hydrology Science and Technology 8: 393–409. DOI: 10.1504/IJHST.2018.095536.
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