FloodModel¶

class FloodModel(trueKs=30.0, trueZv=50.0, trueZm=55.0, distributionHdLow=True)¶

Data class for the flood model.

Parameters:

Lfloat, optional: Length of the river. The default is 5000.0.
Bfloat, optional: Width of the river. The default is 300.0.
trueKsfloat, optional: The true value of the Ks parameter. The default is 30.0.
trueZvfloat, optional: The true value of the Zv parameter. The default is 50.0.
trueZmfloat, optional: The true value of the Zm parameter. The default is 55.0.
distributionHdLowbool, optional: If True, then the distribution of Hd is uniform in [2, 4] i.e the dyke is relatively low. Otherwise, the distribution of Hd is uniform in [7, 9] i.e the dyke is relatively high. The default is True.

Examples

>>> from openturns.usecases import flood_model
>>> # Load the flood model
>>> fm = flood_model.FloodModel()
>>> print(fm.data[:5])
    [ Q ($m^3/s$) H (m)       ]
0 : [  130           0.59     ]
1 : [  530           1.33     ]
2 : [  960           2.03     ]
3 : [ 1400           2.72     ]
4 : [ 1830           2.83     ]
>>> print("Inputs:", fm.model.getInputDescription())
Inputs: [Q, Ks, Zv, Zm, B, L, Zb, Hd]
>>> print("Output:", fm.model.getOutputDescription())
Output: [H, S, C]

Get the height model.

>>> heightInputDistribution, heightModel = fm.getHeightModel()
>>> print("Inputs:", heightModel.getInputDescription())
Inputs: [Q,Ks,Zv,Zm]
>>> print("Outputs:", heightModel.getOutputDescription())
Outputs: [H]

Get the flooding model with high Hd scenario.

>>> fm = flood_model.FloodModel(distributionHdLow=False)

Attributes:

dimThe dimension of the problem: dim=4
QTruncatedDistribution of a Gumbel distribution: ot.TruncatedDistribution(ot.Gumbel(558.0, 1013.0), 0.0, ot.TruncatedDistribution.LOWER)
KsTruncatedDistribution of a Normal distribution: ot.TruncatedDistribution(ot.Normal(30.0, 7.5), 0.0, ot.TruncatedDistribution.LOWER)
ZvUniform distribution: ot.Uniform(49.0, 51.0)
ZmUniform distribution: ot.Uniform(54.0, 56.0)
BUniform distribution: Triangular(295.0, 300.0, 305.0)
LUniform distribution: ot.Triangular(4990.0, 5000.0, 5010.0)
HdUniform distribution: ot.Uniform(54.0, 56.0)
ZbUniform distribution: The distribution depends on distributionHdLow.
modelParametricFunction: The flood model. The function has input dimension 4 and output dimension 1. More precisely, we have $\vect{X} = (Q, K_s, Z_v, Z_m)$ and $Y = H$ . Its parameters are $\theta = (B, L)$ .
distributionJointDistribution: The joint distribution of the input parameters.
dataSample of size 10 and dimension 2: A data set which contains noisy observations of the flow rate (column 0) and the height (column 1).

Methods

getHeightModel([L, B, Zb, Hd])

Return the height model with corresponding input distribution

__init__(trueKs=30.0, trueZv=50.0, trueZm=55.0, distributionHdLow=True)¶

getHeightModel(L=5000.0, B=300.0, Zb=55.5, Hd=3.0)¶

Return the height model with corresponding input distribution

Parameters:

Lfloat, optional: The value of the river length. The default is 5000.0.
Bfloat, optional: The value of the river width. The default is 300.0.
Zbfloat, optional: The level (altitude) of the bank. The default is 55.5.
Hdfloat, optional: The height of the dyke. The default is 3.0.

Returns:

heightInputDistributionot.Distribution(4): The joint input distribution of (Q, Ks, Zv, Zm).
heightModelot.Function(4, 1): The function with (Q, Ks, Zv, Zm) as input and (H) as output.