Hybrid Wavelet-M5 Model Tree for Rainfall-Runoff Modeling
By Vahid Nourani, Alireza Tajbakhsh, A. Molajou, H. Gökçekuş. Journal of hydrologic engineering, 2019
55 citations recorded by the source corpus.
AbstractIn this study, the hybrid wavelet-M5 model was introduced to model the rainfall-runoff process via three different data division strategies (75%–25%, 60%–40%, and 50%–50%) for two different...
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/1e398f6e3a875017c18b055becc688b96d97d1da
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In Environmental Engineering
- Anurag Malik
- Ozgur Kisi
- Quoc Bao Pham
- D. Souag-Gamane
- Y. Tikhamarine
- Jan Adamowski
- Kwok-Wing Chau
- Masoud Karbasi
In Global and Planetary Change
- Ozgur Kisi
- Anurag Malik
- D. Souag-Gamane
- Y. Tikhamarine
- Quoc Bao Pham
- Masoud Karbasi
- Hossein Bonakdari
- Babak Mohammadi
In Water Science and Technology
- Anurag Malik
- D. Souag-Gamane
- Y. Tikhamarine
- Quoc Bao Pham
- Ozgur Kisi
- Masoud Karbasi
- Jan Adamowski
- Ravinesh C. Deo
Citations in the corpus
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, methodology): “time-scales by utilizing hybrid machine learning models (Adnan et al. 2019; Huang et al. 2019; Nourani et al. 2019; Roy and Singh 2019; Xie et al. 2019; Zakhrouf et al. 2019; Tikhamarine et al. 2019b, 2020b), also they report the successful application of hybrid machine learning models for daily or ”