The potential of different ANN techniques in evapotranspiration modelling

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Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs (2014, methodology): “In this study, different number of hidden layer neurons and spread constants are examined for the RBNN models with a simple trial error method adding some loops to the program codes [22,43,44].”
A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran (2018, methodology): “Finally, the processed data was transferred from the hidden layer to the output layer by using linear function (Kisi 2008; Kim et al. 2013).”
Survey of computational intelligence as basis to big flood management: challenges, research directions and future work (2018, unknown): “& Chang, 2014) (Kisi, 2008) (Nourani, Komasi, &”
Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration (2018, background): “plants – depends on several climatological and agricultural interacting factors (Kisi 2008; Rahimikhoob 2016) The evapotranspiration rate from a reference (e.”