Environmental Analysis Tools

This subject is not offered in 2013.

120 hours

Admission to Master of Engineering OR

None

Completion of the following subjects will assist in learning:

None

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

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

Specific topics are:

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

• Principle component analysis
• Factor analysis

4. Analysis of Spatial Data

• Simple spatial interpolations
• Analysis of spatial variability
• Spatial models and Kriging

On completion of this subject students should be able 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

• Two 2500-word reports, due mid-semester and week 12 (90%)
• Four 20-minute quizzes held every three weeks throughout the semester (10%)

None

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