Chapter 7

Question 1

What is model verification?

Answer 1

MV is the process of confirming that the model implements the assumptions of the conceptual model correctly so that the model is a truthful representation of the theoretical abstraction of the system and the mathematical formulation used to describe it.

Question 2

What is meant by modularity?

Answer 2

Modularity is the degree to which a system’s components may be separated and recombined, with the benefit of reducing complexity by breaking the system into smaller interrelated compartments.

Question 3

What else can be done to reduce error?

Answer 3

1. structured walk-through to see if the modeller understands moel correctly
2. preparing documentation

Question 4

What is degeneracy testing?

Answer 4

Degeneracy testing consists of checking that the model works for the extreme values of the parameters.

Question 5

Why are model outputs subject to uncertainties and imprecisions?

Answer 5

1. models are simplification of real systems
2. there are errors and approximations associated with input data
3. input data errors and errors in the model’s structure often interact

Question 6

What is sensitivity analysis?

Answer 6

Sensistivity analysis allows exploring and quantifying the changes in model output values resulting from changes in model input parameters.

Question 7

How does uncertainty analysis differ from sensitivity analysis?

Answer 7

Uncertainty analyses make use of probabilistic descriptions of model inputs to derive probability distributions of model predictions.

Uncertainty describe the range of potential outcomes with their associated probability of occurrance.

Question 8

What are the types of uncertainty?

Answer 8

Knowledge uncertainty:
1. Structural uncertainty - imperfect representation of the process
2. Parameter uncertainty - imperfect knowledge of parameter values

Question 9

What are random variables?

Answer 9

Variables that cannot be predicted 100% sure. The probability can be described by a probability distribution.

Question 10

What equation describes sensitivity analysis?

Answer 10

SI = [Y(P_0 + delta_P) - Y(P_0 - delta_P)]/2delta_P

Y= output variable
P= input parameter
SI= sensitivity index

Question 11

What equation describes elasticity index?

Answer 11

EI_PY = (P_0 / (Y (P_0)))*SI_PY

Elasticity analysis measures the relative change in output Y for a relative change in input P.

Question 12

What is an assumption of sensitivity analysis?

Answer 12

That the parameter sets are independent

Question 13

What is model calibration?

Answer 13

Calibration is the step that connects the model with the studied system. Once data is available and model structure has been defined, calibration involves the identification of parameter values.

Question 14

What is over and underfitting?

Answer 14

When a model is overfitted its prediction follows a particular set of data closely and may fail to fit additional data or predict future observations correctly. Parameter estimation and model evaluation should be separate and independent.

Question 15

What are identifiable and non identifiable models?

Answer 15

Identifiable models are models which parameters can all be uniquely identified.
Unidentifiable models are models in which at least one parameter is nonidentifiable.