Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression

By Hossein Tabari, Safar Marofi, A. Sabziparvar. Irrigation science, 2010

146 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/dd2b26591fac7b7416e714f065306a83222e64cb

How researchers see this paper

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

In Atmospheric Science

1. Babak Mohammadi
2. Ozgur Kisi
3. Roozbeh Moazenzadeh
4. Kwok-Wing Chau
5. Shahaboddin Shamshirband
6. Vijay P. Singh
7. Dalibor Petkovic
8. Pijush Samui

In Environmental Engineering

1. Ozgur Kisi
2. Babak Mohammadi
3. Ravinesh C. Deo
4. Kwok-Wing Chau
5. Zaher Mundher Yaseen
6. Shahaboddin Shamshirband
7. Mohammad Ali Ghorbani
8. Roozbeh Moazenzadeh

In Global and Planetary Change

1. Ozgur Kisi
2. Babak Mohammadi
3. Zaher Mundher Yaseen
4. Ravinesh C. Deo
5. Pijush Samui
6. Mohammad Ali Ghorbani
7. Dookie Kim
8. Vahid Karimi

Citations in the corpus

• Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models (2016, methodology): “When the sensitivity of the precipitation features was tested, the lowest contribution (\10 %) was obtained for all three machine learning models, as was also the observation in an earlier study (Tabari et al. 2010).”
• Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey (2018, methodology): “Different studies also used the standalone MLP model for solving other environmental problems such as the estimation of pan evaporation (Kişi 2009a; Tabari et al. 2010), comparison of soil and water assessment tools for watershed regions (Singh et al.”
• Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns (2013, methodology): “…in the field of evaporation have been obtained (Bruton et al. 2000; Sudheer et al. 2002; Terzi and Keskin 2005; Keskin and Terzi 2006; Kisi 2006; Tan et al. 2007; Kisi 2009; Tabari et al. 2009; Shirsath and Singh 2010; Guven and Kisi 2011; Shiri et al. 2011; Shiri and Kisi 2011; Kim et al. 2012).”
• Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran (2018, background): “Tabari et al. (2010) reported better performance in an ANN compared to nonlinear regression (with RMSE values of 0.42 and 0.92, respectively) for their evaporation predictions at five meteorological stations in Hamadan province, Iran over the period 1996 to 2005.”
• Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration (2016, background): “…logistics activities (Mehrsai et al. 2013), mathematics (Kumar and Yadav 2011), computer science (Harpham and Dawson 2006; Fernández-Navarro et al. 2011; Wang et al. 2012), and meteorological and hydrological studies (Kagoda et al. 2010; Tabari et al. 2010; Marofi et al. 2011; Florido et al. 2012).”