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Environmental Analysis Tools (ENEN90032)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Dr Dongryeol Ryu
Overview
| Availability | Semester 2 - Dual-Delivery |
|---|---|
| Fees | Look up fees |
AIMS
The aim of this subject is to help students develop capability to effectively summarise environmental variables met in the course of research and design, to select appropriate statistical models describing the data structure, and to conduct statistical inference on underlying processes. Students will apply a variety of models from a conventional or Bayesian approach to solve the problems at hand and derive deterministic or stochastic inferences from them.
The subject is composed of four wide-ranging topics from exploratory data analysis to spatial modelling. At the beginning of each topic, students are provided with a set of data from environmental research, and a number of analysis tools are conveyed in the lectures. The mathematical aspects of the subject build on concepts developed in fundamental engineering mathematics and statistics courses from undergraduate courses. It supports student learning in the capstone design and research projects where data analysis skills are assumed.
The subject provides a fundamental skill for a career in environmental engineering where the ability to analyse and communicate the meaning of time series and spatial data sets are expected.
INDICATIVE CONTENT
Specific topics include:
1. Exploratory Data Analysis
- Summary statistics and probability models
- Analysis of variability and hypothesis test
- Linear regression and verification/validation.
2. Time Series Analysis
- Introduction to multivariate analysis
- Principle component analysis
- Stochastic forecast and verification.
3. Methods for Multivariate Data
- Multivariate linear regression
- Principle component analysis.
4. Analysis of Spatial Data
- Simple spatial interpolations
- Analysis of spatial variability
- Spatial models and Kriging.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
- Effectively summarise their analysis and design outputs
- Use stochastic approach to make statistical inference about random environmental variables
- Define and evaluate objective functions for their design target
- Quantitatively test their hypothesis
- Select the most appropriate statistical model describing the data at hand
- Generate both deterministic and stochastic realisations of environmental variables.
Generic skills
- Ability to apply knowledge of science and engineering fundamentals
- Ability to undertake problem identification, formulation, and solution
- Proficiency in engineering design
- Ability to conduct an engineering project.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Admission to MC-ENG Master of Engineering OR
Admission to the 206EC Master of Environmental Engineering OR
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST20029 | Engineering Mathematics |
Semester 1 (Dual-Delivery - Parkville)
Summer Term (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
OR both of the following subjects:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| MAST10005 | Calculus 1 |
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
| MAST10007 | Linear Algebra |
Summer Term (Dual-Delivery - Parkville)
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
Recommended background knowledge
Completion of the following subjects will assist in learning:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| CVEN30008 | Engineering Risk Analysis |
Semester 1 (Online)
Semester 2 (Dual-Delivery - Parkville)
|
12.5 |
| CVEN30010 | Systems Modelling and Design |
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Online)
|
12.5 |
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
| Description | Timing | Percentage |
|---|---|---|
Two 2500-word reports due mid-semester and week 12, each assignment will require approximately 55 hours of work. Intended Learning Outcomes (ILOs) 1 to 6 are addressed in these reports
| Throughout the teaching period | 90% |
Four 20-minute quizzes held every three weeks throughout the semester. ILOs 1 to 6 are addressed in the quizzes
| Throughout the teaching period | 10% |
Last updated: 3 November 2022
Dates & times
- Semester 2
Principal coordinator Dongryeol Ryu Mode of delivery Dual-Delivery (Parkville) Contact hours 48 hours (Lectures: 2 hours per week; Tutorials: 2 hours per week) Total time commitment 200 hours Teaching period 26 July 2021 to 24 October 2021 Last self-enrol date 6 August 2021 Census date 31 August 2021 Last date to withdraw without fail 24 September 2021 Assessment period ends 19 November 2021 Semester 2 contact information
Dr Dongryeol Ryu
Time commitment details
200 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
Recommended texts and other resources
Chris Chatfield (2004). The Analysis of Time Series: An Introduction. Chapman & Hall. Boca Raton, FL
Wilks, D.S. (2011). Statistical Methods in the Atmospheric Sciences. Elsevier, Amsterdam, The Netherlands.
Kitanidis, P.K. (1997). Introduction to Geostatistics: Applications to Hydrogeology. Cambridge University Press, Cambridge; New York
Ramsey, Fred L. & Daniel W. Schafer. (2013). The Statistical Sleuth: A Course in Methods of Data Analysis. Brooks/Cole Cengage Learning, Boston, MA - Subject notes
LEARNING AND TEACHING METHODS
Key analysis methods are introduced in lectures, which are then followed up in tutorial and computer based exercises. The tutorial use problem based learning techniques. The computer based exercises use MatLab as the main software tool, which is used throughout the course.
INDICATIVE KEY LEARNING RESOURCES
Chris Chatfield (2004). The Analysis of Time Series: An Introduction. Chapman & Hall. Boca Raton, FL
Wilks, D.S., (2011). Statistical Methods in the Atmospheric Sciences. Elsevier, Amsterdam, The Netherlands.
Kitanidis, P.K. (1997). Introduction to Geostatistics: Applications to Hydrogeology. Cambridge University Press, Cambridge; New York
Ramsey, Fred L. & Daniel W. Schafer (2013). The Statistical Sleuth: A Course in Methods of Data Analysis. Brooks/Cole Cengage Learning, Boston, MA
Anon (n.d.) Learn Matlab. http://aaee-scholar.pbworks.com/w/page/1177071/Learn%20MATLABCAREERS / INDUSTRY LINKS
Real data sets from industry and research partners form the basis of the assignments and learning activities. Industry standard computation software (MatLab) is used for assignments.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Philosophy - Engineering Course Doctor of Philosophy - Engineering Course Master of Environmental Engineering Course Ph.D.- Engineering Major Tailored Specialisation Major Tailored Specialisation Major Waste Management Specialisation (formal) Environmental Major Energy Efficiency Modelling and Implementation Major Energy Studies Major Integrated Water Catchment Management Major Integrated Water Catchment Management Specialisation (formal) Spatial Major Tailored Specialisation Major Waste Management Major Energy Efficiency Modelling and Implementation Major Energy Studies - Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022