SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment

By Hossein Tabari, Ozgur Kisi, A. Ezani, P. H. Talaee. 2012

302 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Plant Science, Soil Science

View Paper: https://www.semanticscholar.org/paper/6f809eb0f791a8841c59a30040ba5600590374c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. Mohammad Valipour
2. Shahaboddin Shamshirband
3. Xiaohu Wen
4. Zhi-bin He
5. Jianhua Si
6. Dalibor Petkovic
7. Chaopeng Shen
8. H. Shao

In Plant Science

1. Chaopeng Shen

In Soil Science

1. M. Khoshravesh
2. Mohammad Ali Gholami Sefidkouhi
3. Chaopeng Shen
4. Guillermo Marcillo
5. Fernando E. Miguez

Citations in the corpus

• Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments (2017, background): “…et al. 2013; Yao et al. 2014), solar radiation (Iizumi et al. 2012), and streamflow modeling (Block and Rajagopalan 2009; Liang et al. 2013), there are few studies about application of them in estimation of ET (Bachour et al. 2014; Izadifar and Elshorbagy 2010; Tabari et al. 2012; Zhu et al. 2013).”
• Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions (2015, background): “Evapotranspiration (ET) mainly controls several hydrological processes and its accurate estimation provides valuable information for water resources planning and management (Tabari et al. 2012), particularly in the arid area (Laaboudi et al.”
• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “, 2006), evapotranspiration (Tabari et al., 2012), lake level (Khan & Coulibaly, 2006), groundwater level (Yoon et al.”
• Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration (2016, background): “Support vector machine (SVM) is one of the novel soft computing learning algorithms, which has gained importance in forecasting problems of ET0 (Kisi and Cimen 2009; Kisi 2011b, 2013; Tabari et al. 2012; Shiri et al. 2014; Gocic et al. 2015).”