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Compute all available model metrics

eval_all.Rd

computes all evaluation metrics given a prediction and a reference vector. The functionality includes general regression metrics and hydrological metrics (wrapper to (Mauricio Zambrano-Bigiarini 2020) ):

  • Root Mean Square Error (RMSE)

  • Normalized Root Mean Square Error (NRMSE)

  • Coefficient of Determination (R2)

  • Nash-Sutcliffe Efficiency (Nash and Sutcliffe 1970)

  • Kling-Gupta Efficiency (Gupta et al. 2009)

Usage

eval_all(prediction, reference)

rmse(prediction, reference)

nrmse(prediction, reference)

r2(prediction, reference)

nse(prediction, reference)

kge(prediction, reference)

Arguments

prediction

a vector or ts object of predicted values

reference

a vector or ts object (on the same time scale as prediction) containing ground truth values

Functions

  • rmse(): Root Mean Squared Error

  • nrmse(): Normalized Root Mean Squared Error

  • r2(): Coefficient of Determination

  • nse(): Nash-Sutcliffe Efficiency

  • kge(): Kling-Gupta Efficiency

References

Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009). “Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling.” Journal of Hydrology, 377(1-2), 80--91. doi:10.1016/j.jhydrol.2009.08.003 .

Mauricio Zambrano-Bigiarini (2020). hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. doi:10.5281/zenodo.839854 , R package version 0.4-0, https://github.com/hzambran/hydroGOF.

Nash JE, Sutcliffe JV (1970). “River flow forecasting through conceptual models part I — A discussion of principles.” Journal of Hydrology, 10(3), 282--290. doi:10.1016/0022-1694(70)90255-6 .

See also

hydroGOF