2020
DOI: 10.1007/s00024-020-02473-5
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Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and Machine Learning Algorithms in Four Climatic Regions

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Cited by 39 publications
(19 citation statements)
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“…Several factors including hidden/output layer, weights and neurons, activation functions, network and other tuning parameters helped in determining suitable model. These factors are explicitly explained in Raza et al [17][18]. It was noted that these models produced good results using fewer inputs which resembled with field calculations.…”
Section: Development Of Soft Computing Modelsmentioning
confidence: 97%
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“…Several factors including hidden/output layer, weights and neurons, activation functions, network and other tuning parameters helped in determining suitable model. These factors are explicitly explained in Raza et al [17][18]. It was noted that these models produced good results using fewer inputs which resembled with field calculations.…”
Section: Development Of Soft Computing Modelsmentioning
confidence: 97%
“…The structure of soft computing model depends upon its input/output dataset, neurons and activation [17], neurons in input/hidden/output layers accounted preeminent factor for determining structure of model. Alternatively, the structure in tree based soft computing models could be determined by considering its depth, size and level [18]. However, the models based on gene expression programming (GEP) formulated an equation for determining input-output relationship.…”
Section: Structurementioning
confidence: 99%
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“…V-fold cross-validation has been currently used to test and validate the input data. The SCG parameters which require to run the developed model are given in Table 2 (Raza, 2020).…”
Section: Granger Causality Testmentioning
confidence: 99%
“…Several comparative studies have been conducted to determine the best method for calculating evapotranspiration [8][9][10][11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%