COURSE UNIT TITLE

: MULTIVARIATE TECHNIQUES IN HYDROLOGY

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CIE 5118 MULTIVARIATE TECHNIQUES IN HYDROLOGY ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

Offered to

HYDRAULIC ENGINEERING AND WATER RESOURCES
HYDRAULIC ENGINEERING AND WATER RESOURCES
HYDRAULIC ENGINEERING AND WATER RESOURCES

Course Objective

Multivariate hydrometric data consist of multi-site observations of a single variable,
of several variables observed at a single site, or of both. Essentially, the vast
majority of hydrologic data is multivariate, although introductory statistical
hydrology courses naturally concentrate on the simpler problems raised by observations
of a single variable. Methods of analyzing multivariate data constitute an increasingly
important area of hydrologic analyses. The proposed course will introduce basic
techniques of multivariate analysis and their application to water resources problems.

Learning Outcomes of the Course Unit

1   to interprete and analyze multi-variate hydrometeorological data
2   to synthesize information from multi-variate hydrometeorological data
3   to be able to use multivariate statistical analysis techniques in water resources
4   to interprete and analyze different dimensions of available data
5   to be able to apply multi-variate analysis techniques to water resources

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Fundamentals of Multivariate Analysis
2 Principal Component Analysis
3 Factor Analysis
4 Multidimensional Scaling
5 Cluster Analysis
6 Data Analysis Problems
7 Cross-Classified Frequency Data
8 Midterm exam
9 Canonical Correlation Analysis
10 Discriminant Analysis
11 Multiple Discriminant Analysis
12 Linear Structural relations
13 Latent Structure Analysis
14 Case Studies and Assessment

Recomended or Required Reading

Chatfield, C., Collins A.J., Introduction to Multivariate Analysis, Chapman and Hall,
London, 1980.
Dillon, W.R., Goldstein, M., Multivariate Analysis: Methods and Applications, John
Wiley and Sons, 1984.
Jobson, J.D., Applied Multivariate Data Analysis (Volumes I and II), Springer-Verlag,
1992.

Planned Learning Activities and Teaching Methods

Lecture notes, presentations, and source books are the main material. The students are
obligated to prepare a project assignment.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.25 + ASG *0.25 +FIN *0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG *0.25 +RST *0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

There will be 1 midterm exam and 1 homework assignment in determining the in-semester
grade.

Language of Instruction

English

Course Policies and Rules

Project assignment is obligatory and a prequisite for final exam

Contact Details for the Lecturer(s)

nilgun.harmancioglu@deu.edu.tr

Office Hours

9:00 to 17:00 in week

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 4 52
Preparation for midterm exam 1 24 24
Preparation for final exam 1 24 24
Preparing assignments 1 48 48
Final 1 4 4
Midterm 1 4 4
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.13
LO.23
LO.33
LO.43
LO.54

CONTACT INFORMATION
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Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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