Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment
By A. Rahimikhoob. Water resources management, 2014
74 citations recorded by the source corpus.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/228851be9b3f012c0010a5b890dac20b6b624eb9
How researchers see this paper
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In Environmental Engineering
- Mohammad Zounemat‐Kermani
- Behrooz Keshtegar
- Hamed Ghohani Arab
- Ozgur Kisi
- Jan Adamowski
- Kwok-Wing Chau
- Masoud Karbasi
- Ravinesh C. Deo
In Global and Planetary Change
- Mohammad Zounemat‐Kermani
- Hamed Ghohani Arab
- Behrooz Keshtegar
- Ozgur Kisi
- Masoud Karbasi
- Hossein Bonakdari
- Babak Mohammadi
- Babak Jahani
In Water Science and Technology
- Mohammad Zounemat‐Kermani
- Hamed Ghohani Arab
- Behrooz Keshtegar
- Ozgur Kisi
- Masoud Karbasi
- Jan Adamowski
- Ravinesh C. Deo
- Kwok-Wing Chau
Citations in the corpus
- Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration (2018, methodology): “However, the application of the M5model tree technique, which is categorized as a machine learningmethod to estimate the evapotranspiration is limited in the literature (Kisi 2016; Pal and Deswal 2009; Rahimikhoob 2014; Rahimikhoob 2016).”