Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN)
By A. Baba, Jalal Shiri, Ozgur Kisi, A. F. Fard, Sungwon Kim, R. Amini. 2013
71 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/670f691d602a24bb3f076ad7fb26410e1123f78a
How researchers see this paper
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In Artificial Intelligence
- Mohammad Zounemat‐Kermani
- Behrooz Keshtegar
- Ozgur Kisi
- Jan Adamowski
- Kwok-Wing Chau
- C. Wu
- Yan Li
- K. Sun
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
Citations in the corpus
- Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration (2018, methodology): “In addition to ANNs, several ANFIS models, which are based on fuzzy logic concepts in the soft computing techniques have been applied to evapotranspiration modeling (Citakoglu et al. 2014; Cobaner 2011; Dogan 2009; Kisi 2016; Kisi and Zounemat-Kermani 2014; Petković et al. 2015; Pour-Ali Baba et al.”