Lecture 4

Question 1

Model Comparison: Which model would you trust more?

Answer 1

Question 2

R², AIC, and Model Fitting

Answer 2

Question 3

Calibration in Models

Answer 3

Question 4

Why Calibration is Needed

Answer 4

Question 5

Parameter Sensitivity

Answer 5

Question 6

Calibration Techniques

Answer 6

Question 7

Measurement of Deviation

What are the key metrics used to measure deviation in model performance?

Answer 7

Question 8

Mean Absolute Error (MAE)

What does it represent?

Answer 8

Question 9

Mean Absolute Percent Error (MA%E)

What does it indicate?

Answer 9

Question 10

Root Mean Square Error (RMSE)

what does it emphasize?

Answer 10

Question 11

Answer 11

Question 12

Allometric Relationships

Answer 12

Question 13

Automatic Calibration Algorithms

Answer 13

Question 14

Nash-Sutcliffe Efficiency

Answer 14

Question 15

Model Optimization

Answer 15

Question 16

Efficiency and Model Usefulness

Answer 16

Question 17

What is the purpose of calibration?

Answer 17

To adjust parameters to minimize the difference between model predictions and observed data.

Question 18

What steps are involved in calibration?

Answer 18

• Choose initial parameter values.
• Iteratively adjust parameters to minimize errors.
• Evaluate the fit using metrics like R-squared and RMSE.

Question 19

What is cross-validation?

Answer 19

A method that divides datasets into training and testing subsets for evaluation.

Question 20

What is k-fold cross-validation?

Answer 20

A repeated cross-validation process that reduces bias and improves reliability.