Estimating Evapotranspiration using Artificial Neural Network

By M. Kumar, Narendra Singh Raghuwanshi, W. Wallender, W. Pruitt. 2002

530 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/5192a72c342d14d711960ca498766e23cf863c27

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In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression (2010, methodology): “Artificial neural networks (ANNs) are being increasingly used in E and evapotranspiration (ETo) modeling (Kumar et al. 2002; Sudheer et al. 2003; Trajkovic et al. 2003; Keskin and Terzi 2006; Deswal and Pal 2008; Rahimi Khoob 2009).”
Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration (2016, background): “…(RBF) (Trajkovic et al. 2000; Kim and Kim 2008), the generalized regression neural networks (GRNN) (Cigizoglu 2005; Kisi 2006), backpropagation (Kumar et al. 2002; Huo et al. 2012), Levenberg– Marquardt (Khoob 2008; Zanetti et al. 2007; Tabari and Hosseinzadeh Talaee 2013), and conjugate…”
Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration (2018, methodology): “In this respect, the use of ANNs for prediction of ET0 as a function of climatic has recently been reported and made available in lots of articles and researches (Aghajanloo et al. 2013; Guven et al. 2008; Kisi 2016; Kumar et al. 2008; Kumar et al. 2002; Traore et al. 2016; Zanetti et al. 2007).”
Estimating Daily Pan Evaporation Using Different Data-Driven Methods and Lag-Time Patterns (2013, background): “Some of investigators have tried to estimate evaporation through several indirect methods based on different weather variables, but some of these methods require data which cannot be easily obtained (Marino et al. 1993; Rosenberry et al. 2007).”
Generalized regression neural networks for evapotranspiration modelling (2006, methodology): “Kumar et al. (2002) used a multi-layer perceptron ANN (MLP) with backpropagation training algorithm for the estimation of ET0.”
Estimation of Monthly Mean Reference Evapotranspiration in Turkey (2013, methodology): “In the past decades, the artificial neural network (ANN) approach has been used to model reference evapotranspiration (e.g., Kumar et al. 2002; Sudheer et al. 2003; Trajkovic et al. 2003; Kisi 2006a, b, 2007; Rahimikhoob 2008, 2010).”
Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments (2017, background): “…such as irrigation system design and management, hydrologic water balance, crop yield simulation, irrigation scheduling, drainage studies, agricultural and forest meteorology, and water resources planning and management (Banihabib et al. 2012; Kumar et al. 2002; Valipour 2014c, d, e, f, g, h).”
Comparison between M5 Model Tree and Neural Networks for Estimating Reference Evapotranspiration in an Arid Environment (2014, methodology): “Some of them used the same climatic data required for application of the FAO PM method (Odhiambo et al. 2001; Kumar et al. 2002; Trajkovic et al. 2003).”