smash.evaluation#

smash.evaluation(model, metric='nse', start_eval=None, end_eval=None)[source]#

Evaluate the Model efficiency/error based on observed and simulated discharges.

Parameters:

modelModel

Primary data structure of the hydrological model smash.

metricstr or list[str], default ‘nse’

The efficiency or error metric. Should be one of

‘nse': Nash-Sutcliffe Efficiency
‘nnse': Normalized Nash-Sutcliffe Efficiency
‘kge': Kling-Gupta Efficiency
‘mae': Mean Absolute Error
‘mape': Mean Absolute Percentage Error
‘mse': Mean Squared Error
‘rmse': Root Mean Squared Error
‘lgrm': Logarithmic

start_evalstr, pandas.Timestamp or None, default None

The start of the evaluation period. The value can be a string that can be interpreted as pandas.Timestamp. The start_eval date value must be between the start time and the end time defined in Model.setup.

Note

If not given, the start of evaluation period will be defined as the start time in Model.setup.

end_evalstr, pandas.Timestamp or None, default None

The end of the evaluation period. The value can be a string that can be interpreted as pandas.Timestamp. The end_eval date value must be between the start time and the end time defined in Model.setup.

Note

If not given, the end of evaluation period will be defined as the end time in Model.setup.

Returns:

metricsnumpy.ndarray: An array of shape (ng, nm) representing the computed metrics for ng gauges and nm evaluation metric.

Examples

>>> from smash.factory import load_dataset
>>> setup, mesh = load_dataset("cance")
>>> model = smash.Model(setup, mesh)

Optimize Model

>>> model.optimize()
</> Optimize
    At iterate     0    nfg =     1    J = 6.95010e-01    ddx = 0.64
    At iterate     1    nfg =    30    J = 9.84107e-02    ddx = 0.64
    At iterate     2    nfg =    59    J = 4.54087e-02    ddx = 0.32
    At iterate     3    nfg =    88    J = 3.81818e-02    ddx = 0.16
    At iterate     4    nfg =   117    J = 3.73617e-02    ddx = 0.08
    At iterate     5    nfg =   150    J = 3.70873e-02    ddx = 0.02
    At iterate     6    nfg =   183    J = 3.68004e-02    ddx = 0.02
    At iterate     7    nfg =   216    J = 3.67635e-02    ddx = 0.01
    At iterate     8    nfg =   240    J = 3.67277e-02    ddx = 0.01
    CONVERGENCE: DDX < 0.01

Compute multiple evaluation metrics for all catchments

>>> smash.evaluation(model, metric=["mae", "mse", "nse", "kge"])
array([[ 3.16766095, 44.77915192,  0.96327233,  0.94864655],
       [ 1.07711864,  4.36171055,  0.90502125,  0.84566253],
       [ 0.33053449,  0.50542408,  0.84976768,  0.8039571 ]])

Add start and end evaluation dates

>>> model.setup.start_time, model.setup.end_time
('2014-09-15 00:00', '2014-11-14 00:00')
>>> start_eval, end_eval = "2014-09-30 00:00", "2014-11-01 00:00"

Compute the Nash-Sutcliffe Efficiency for the entire period and for the specified evaluation period

>>> smash.evaluation(model, metric="nse")
array([[0.96327233],
       [0.90502125],
       [0.84976768]])
>>> smash.evaluation(model, metric="nse", start_eval=start_eval, end_eval=end_eval)
array([[0.94048417],
       [0.8667959 ],
       [0.76593578]])

smash.evaluation#

This Page