FloodModel¶

class FloodModel(L=5000.0, B=300.0, trueKs=30.0, trueZv=50.0, trueZm=55.0)¶

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.

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]
>>> print("Parameters:", fm.model.getParameterDescription())
Parameters: [B,L]
>>> print("Outputs:", fm.model.getOutputDescription())
Outputs: [H]

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)
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 $\theta = (B, L)$ .
distributionComposedDistribution: 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).