Evaluating the support vector machine for suspended sediment load forecasting based on gamma test
By S. Rashidi, Mehdi Vafakhah, E. K. Lafdani, M. Javadi. Arabian Journal of Geosciences, 2016
49 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/2513a8247e2d9f7634b295aaa1215f0f725cbac9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- Ozgur Kisi
- Anurag Malik
- Anil Kumar
- Jan Adamowski
- Kwok-Wing Chau
- Masoud Karbasi
- Ravinesh C. Deo
- Mohammad Zounemat‐Kermani
In Global and Planetary Change
- Ozgur Kisi
- Anurag Malik
- Anil Kumar
- Masoud Karbasi
- Hossein Bonakdari
- Salim Heddam
- Babak Mohammadi
- Babak Jahani
In Water Science and Technology
- Anurag Malik
- Anil Kumar
- Ozgur Kisi
- Masoud Karbasi
- Jan Adamowski
- Ravinesh C. Deo
- Kwok-Wing Chau
- Mohammad Zounemat‐Kermani
Citations in the corpus
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, unknown): “In recent times, to overcome this problem Gamma test (GT) has been utilized widely in various fields (Piri et al. 2009; Kakaei Lafdani et al. 2013; Rashidi et al. 2016; Malik et al. 2019b).”
- Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test (2018, methodology): “Datadriven methods provided better accuracy than the HGS and SS. Rashidi et al. (2016) successfully used GT in application of support vector machine (SVM) with RBF and a polynomial function in modeling daily suspended sediment (SS) load of Korkorsar basin, Iran.”