nrmse_iqr

HydroErr.HydroErr.nrmse_iqr(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False)

Compute the IQR normalized root mean square error between the simulated and observed data.

../_images/NRMSE_IQR.png

Range: 0 ≤ NRMSE < inf.

Notes: This metric is the RMSE normalized by the interquartile range of the observed time series (x). Normalizing allows comparison between data sets with different scales. The NRMSEquartile is the least sensitive to outliers of the three normalized rmse metrics.

Parameters:
  • simulated_array (one dimensional ndarray) – An array of simulated data from the time series.
  • observed_array (one dimensional ndarray) – An array of observed data from the time series.
  • replace_nan (float, optional) – If given, indicates which value to replace NaN values with in the two arrays. If None, when a NaN value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • replace_inf (float, optional) – If given, indicates which value to replace Inf values with in the two arrays. If None, when an inf value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • remove_neg (boolean, optional) – If True, when a negative value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
  • remove_zero (boolean, optional) – If true, when a zero value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
Returns:

The IQR normalized root mean square error.
Return type:

float

Examples

>>> import HydroErr as he
>>> import numpy as np

>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.nrmse_iqr(sim, obs)
0.2595461185212093

References

  • Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple resolution comparison between maps that share a real variable. Environmental and Ecological Statistics 15(2) 111-142.