Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments

By A. Ritter, R. Muñoz‐Carpena. 2013

872 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/f48cbb4ed9e4190ba8911fb68e60a5f8f5b12efc

How researchers see this paper

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

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Comparing expert judgement and numerical criteria for hydrograph evaluation (2015, methodology): “Others have advised standardizing evaluation tools to make published studies more consistent in terms of evaluation (Dawson et al. 2007, Moriasi et al. 2007, Schaefli and Gupta 2007, Reusser et al. 2009, Alexandrov et al. 2011, Ritter and Muñoz-Carpena 2013).”
Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry (2017, background): “Critical values for satisfactory performance were taken as d > 0.8 for plant properties or d > 0.60 for soil properties, NSE > 0.65, and R2 > 0.70 (Dawson et al., 2007; Ritter and Muñoz-Carpena, 2013).”
A rational performance criterion for hydrological model (2020, positive): “However, the distribution of NSE has not been known exactly (McCuen et al., 2006; Ritter and Muñoz-Carpena, 2013; Liu et al., 2018), and the distribution of KGE seems to be impossible to be derived except by the “bootstrap” method.”
Modelling of the mechanical properties of concrete with cement ratio partially replaced by aluminium waste and sawdust ash using artificial neural network (2019, background): “It can take values from 0 to 1, the closer the value is to 1, the better explanatory power of the independent variable [52].”
Application of response surface methodology: Predicting and optimizing the properties of concrete containing steel fibre extracted from waste tires with limestone powder as filler (2019, background): “MPE ¼ P Yi Oið Þ n ð2Þ MSE ¼ P Yi Oið Þ2 n ð3Þ RMSE ¼ MSEð Þ1=2 ð4Þ NSE ¼ 1 1 ht þ 1 2 ð5Þ Where nt ¼ SDRMSE 1 ð6Þ (Source: [31]; Sojobi et al. [32]) Where Yi is the predicted value, Oi is the observed (experimental) value, n is the number of data set, nt is the number of times the standard deviatio”