cE6511: Soft computing for hydrologists
This is a 2-credit lab course covering advanced material on hydrology-related computing methods.
Who should attend?
Primarily geared towards graduate students in water resources engineering as I use the PDM model as the test model that is applied to the system of interest, watersheds. However, the methods I cover are generic enough to be applied to a wide array of modelling and data analysis problems.
Prerequisites
Familiarity with a programming language such as Matlab, Python, R, or equivalent is preferred (though not mandatory).
Course Goals
1. Apply systems concept to improve understanding of your research problem
2. Learn the process of setting up, and calibrating a (hydrologic) model: we will learn at least 4 different ways to calibrate the model!
3. Learn to apply uncertainty and sensitivity analysis on the model
Tentative schedule
Course Materials
The course will access concepts from several textbooks and published journal articles.
Textbook: Rainfall-runoff modelling: The Primer, Keith Beven
Other reference books
Sample assignment
Who should attend?
Primarily geared towards graduate students in water resources engineering as I use the PDM model as the test model that is applied to the system of interest, watersheds. However, the methods I cover are generic enough to be applied to a wide array of modelling and data analysis problems.
Prerequisites
Familiarity with a programming language such as Matlab, Python, R, or equivalent is preferred (though not mandatory).
Course Goals
1. Apply systems concept to improve understanding of your research problem
2. Learn the process of setting up, and calibrating a (hydrologic) model: we will learn at least 4 different ways to calibrate the model!
3. Learn to apply uncertainty and sensitivity analysis on the model
Tentative schedule
Course Materials
The course will access concepts from several textbooks and published journal articles.
Textbook: Rainfall-runoff modelling: The Primer, Keith Beven
Other reference books
- Environmental Modelling: An Uncertain Future, Keith Beven
- Doing Bayesian Data Analysis: A tutorial with R and BUGS, John Kruschke
Sample assignment