Ensemble models

This module implements various ensemble techniques to combine the predictions of multiple Wave runup models.

EnsembleRaw

Returns predicitons from each wave runup model

EnsembleMean

Returns the mean parameter given by all the runup models.
class ensembles.EnsembleRaw(Hs=None, Tp=None, beta=None, Lp=None, r=None)

Returns predicitons from each wave runup model

This class runs predictions on all available runup models and returns the results from each model, i.e. no combining or ensembling is performed. It is provided as a base class for other ensemble models to inherit, they will need access to all predicitions anyway.

Parameters

  • Hs (float or list) – Significant wave height. In order to account for energy dissipation in the nearshore, transform the wave to the nearshore, then reverse-shoal to deep water.

  • beta (float or list) – Beach slope. Typically defined as the slope between the region of \(\pm2\sigma\) where \(\sigma\) is the standard deviation of the continuous water level record.

  • Tp (float or list) – Peak wave period. Must be defined if Lp is not defined.

  • Lp (float or list) – Peak wave length Must be definied if Tp is not defined.

  • r (float or list) – Hydraulic roughness length. Can be approximated by \(r=2.5D_{50}\).

estimate(param)
Returns

Returns a pandas dataframe where each column contains the estimates returned by each runup model

Parameters

param (str) – R2, setup, sig, sinc or swash

Examples: Get a dataframe containing all wave runup model predictions for Hs=4, Tp=11 and beta=0.1.

>>> from py_wave_runup.ensembles import EnsembleRaw
>>> ensemble = EnsembleRaw(Hs=4, Tp=11, beta=0.1)
>>> ensemble_r2 = ensemble.estimate('R2')
>>> ensemble_r2
   Stockdon2006_R2  Power2018_R2  ...  Senechal2011_R2  Beuzen2019_R2
0        2.542036           NaN  ...         1.972371       2.181613

[1 rows x 9 columns]
class ensembles.EnsembleMean(Hs=None, Tp=None, beta=None, Lp=None, r=None)

Returns the mean parameter given by all the runup models.

Parameters

  • Hs (float or list) – Significant wave height. In order to account for energy dissipation in the nearshore, transform the wave to the nearshore, then reverse-shoal to deep water.

  • beta (float or list) – Beach slope. Typically defined as the slope between the region of \(\pm2\sigma\) where \(\sigma\) is the standard deviation of the continuous water level record.

  • Tp (float or list) – Peak wave period. Must be defined if Lp is not defined.

  • Lp (float or list) – Peak wave length Must be definied if Tp is not defined.

  • r (float or list) – Hydraulic roughness length. Can be approximated by \(r=2.5D_{50}\).

estimate(param)
Returns

Returns the mean parameter given by all the runup models.

Parameters

param (str) – R2, setup, sig, sinc or swash

Examples: Get a pandas series containing the mean wave runup model predictions for Hs=4, Tp=11 and beta=0.1.

>>> from py_wave_runup.ensembles import EnsembleMean
>>> ensemble = EnsembleMean(Hs=[3,4], Tp=[10,11], beta=[0.09,0.1])
>>> ensemble_r2 = ensemble.estimate('R2')
>>> ensemble_r2
0    1.969634
1    2.590786
Name: mean_R2, dtype: float64