2017
DOI: 10.1016/j.agwat.2017.08.003
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Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling

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Cited by 244 publications
(99 citation statements)
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“…The performance of the studied models to estimate was evaluated in terms of the following statistical error tests: coefficient of determination ( 2 ), root mean square error (RMSE), relative root mean square error (RRMSE), Nash-Sutcliffe coefficient (NS), and mean absolute error (MAE), which are defined in the following equations [37,38]:…”
Section: Statistical Evaluationmentioning
confidence: 99%
“…The performance of the studied models to estimate was evaluated in terms of the following statistical error tests: coefficient of determination ( 2 ), root mean square error (RMSE), relative root mean square error (RRMSE), Nash-Sutcliffe coefficient (NS), and mean absolute error (MAE), which are defined in the following equations [37,38]:…”
Section: Statistical Evaluationmentioning
confidence: 99%
“…2) were selected as the observation points. Peformance of Hydrus-2D was evaluated via the root mean square error (RMSE) and the relative root mean square error (RRMSE) (Feng et al, 2017):…”
Section: Discussionmentioning
confidence: 99%
“…The authors in [40] compared SVR with multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in modeling reference evapotranspiration (ETo). Genetic algorithms (GAs) [41] and random forest regression (RFR) [42] have also been used for water needs estimation. This paper presents the development of an irrigation decision support system (IDSS) for irrigation management optimization in citrus trees.…”
Section: Introductionmentioning
confidence: 99%