Environmental Analysis Tools

This subject has the following teaching availabilities in 2016:

200 hours

Admission to MC-ENG Master of Engineering OR

Admission to the 206EC Master of Environmental Engineering OR

Completion of the following subjects will assist in learning:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability

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 (ILO)

On completion of this subject the student is expected to:

1. Effectively summarise their analysis and design outputs
2. Use stochastic approach to make statistical inference about random environmental variables
3. Define and evaluate objective functions for their design target
4. Quantitatively test their hypothesis
5. Select the most appropriate statistical model describing the data at hand
6. Generate both deterministic and stochastic realisations of environmental variables.

• Two 2500-word reports (90%) 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
• Four 20-minute quizzes (10%) held every three weeks throughout the semester. ILOs 1 to 6 are addressed in the quizzes

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

This subject is not available as a breadth subject.

• 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.

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%20MATLAB

CAREERS / 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.