ENN575 Artificial Intelligence in Water Modelling
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | ENN575 |
|---|---|
| Prerequisite(s): | Admission to (EV51 OR EN51 OR EN56 OR EN65 OR EN71 OR EN75) |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $3,672 |
| International unit fee | $5,040 |
Unit Outline: Semester 2 2024, Gardens Point, Internal
| Unit code: | ENN575 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | Admission to (EV51 OR EN51 OR EN56 OR EN65 or EN71 or EN75) |
| Coordinator: | Maziar Gholami Korzani | m.korzani@qut.edu.au |
Overview
The goal of modelling water systems (flood and water quality) is to understand their behaviour and predict future changes, investigate their environmental and socio-economic impacts, and support policy development in the water sector. Recent advancements in AI and technology provide new avenues to smartly achieve the above-mentioned goals. This unit will introduce you to fundamentals of AI and how AI enables the future of flood and water quality prediction. You will also grow your skills in techniques for advanced flood and water quality data analytics that can be applied to develop predictive models for water systems in line with contemporary engineering practice.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply advanced knowledge on artificial intelligence (AI) to solve complex water engineering problems
- Implement AI algorithms using appropriate tools and libraries
- Apply systematic approaches to manage water infrastructures using AI based solutions
- Communicate complex AI based water modelling to diverse audiences in oral and written forms
Content
Learning in this unit is within two modules.
Module 1: Introduction to Applied Artificial Intelligence
- Fundaments of machine learning
- Fundaments of data analysis and processing
- Supervised, unsupervised and reinforcement learning
- Classification and clustering algorithms
Module 2): AI Applications in Water Modelling
- Applications of machine learning techniques in flood modelling
- Applications of machine learning in water quality prediction
Learning Approaches
In this unit you can expect to experience the following activities:
- Videos explaining key concepts, released at the beginning of each week
- 2 hour lecture, where you can engage with experienced engineers in working though problem sets, applying the concepts learned in the videos
- 1 to 2 hour of tutorials/workshops where you will be guided through applied design and data analytics problems, working with experienced engineers and your peers to deepen your skills
- Follow up formative quizzes, readings and online discussions to help you review your learning for the week and clarify any areas of misunderstanding
Feedback on Learning and Assessment
Feedback in this unit will be provided in the following ways:
- Formative oral feedback will be offered by the lecturer and tutors during the semester to assist you in the development of your skills.
- Formative written feedback through criteria sheet grading.
- In addition to CRA (criteria sheet), comments on summative assessment will be provided.
- Generic comments will be provided to the cohort through the Canvas.
Assessment
Overview
Assessment items in this unit have been designed to give you the opportunity to show your learning against the unit learning outcomes. You will work individually to complete an engineering problem solving task in the context of AI. You will work as a member of an effective group to prepare and submit an applied design project report and a presentation, addressing a complex problem in water modelling using AI. You will sit for an examination individually during the central examination period at the end of semester, where you will show your overall learning in the unit.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will be required to analyse aspects of machine learning, data analysis and processing in water modelling. You will be presented with an engineering problem and asked to compare and contrast different data processing techniques and machine learning methods to determine a solution, making a recommendation for implementation.
Assessment: Water Modelling using AI
You will work in a team on an engineering challenge that requires complex water modelling using AI. Your team will present the findings of your work in a written report, accompanied by an engineering presentation of key findings.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
There is no required text book. Contents from latest publications in top-tier water journals will be used and referenced during the lectures.
Risk Assessment Statement
You will undertake lectures and tutorials in the traditional classrooms and lecture theatres. As such, there are no extraordinary workplace health and safety issues associated with these components of the unit.
You will be required to undertake practical sessions in the laboratory under the supervision of the lecturer and technical staff of the School. In any laboratory practicals you will be advised of the requirements of safe and responsible behaviour and will be required to wear appropriate protective items (e.g. closed shoes).
You will undergo a health and safety induction before the commencement of the practical sessions and will be issued with a safety induction card. If you do not have a safety induction card you will be denied access to laboratories.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.EN51 Master of Sustainable Infrastructure
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Sustainable Infrastructure
Relates to: Problem Solving Task - Analyse and evaluate Sustainable Infrastructure problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Problem Solving Task - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Water Modelling using AI - Effectively communicate Sustainable Infrastructure problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Water Modelling using AI - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Water Modelling using AI
EN56 Master of Engineering Technology
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Engineering Technology
Relates to: Problem Solving Task - Analyse and evaluate Engineering Technology problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Problem Solving Task - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Engineering Technology in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Water Modelling using AI - Effectively communicate Engineering Technology problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Water Modelling using AI - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Water Modelling using AI
EN65 Graduate Certificate in Water Modelling
- Demonstrate and apply advanced and specialist discipline knowledge, concepts and practices as they relate to contemporary practice in Water Modelling
Relates to: Problem Solving Task, Water Modelling using AI - Analyse and evaluate Water Modelling problems using technical approaches informed by contemporary practice to achieve innovative, critically informed solutions
Relates to: Problem Solving Task - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Water Modelling in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Water Modelling using AI - Effectively communicate Water Modelling problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Water Modelling using AI - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Water Modelling using AI
EN71 Master of Sustainable Infrastructure with Project Management
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Project Management domains
Relates to: Problem Solving Task - Analyse and evaluate problems in Sustainable Infrastructure and Project Management domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Problem Solving Task - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure and Project Management domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Water Modelling using AI - Effectively communicate problems in Sustainable Infrastructure and Project Management domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Water Modelling using AI - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Water Modelling using AI
EN75 Master of Sustainable Infrastructure with Data Analytics
- Demonstrate and apply advanced and specialist discipline knowledge, concepts, methods and practices as they relate to contemporary practice in Sustainable Infrastructure and Data Analytics domains
Relates to: Problem Solving Task - Analyse and evaluate problems in Sustainable Infrastructure and Data Analytics domains using technical approaches informed by contemporary practice and leading-edge research to achieve evidence based, innovative, critically informed solutions and outcomes
Relates to: Problem Solving Task - Apply innovative, systematic approaches to plan, design, deliver and manage projects in Sustainable Infrastructure and Data Analytics domains in a way that assures sustainable outcomes and strategic objectives over their whole lifecycle
Relates to: Water Modelling using AI - Effectively communicate problems in Sustainable Infrastructure and Data Analytics domains, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Water Modelling using AI - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability, reflective practice, risk-informed judgements, and leadership
Relates to: Water Modelling using AI
EN79 Graduate Diploma in Engineering Studies
- Demonstrate and apply advanced discipline knowledge, concepts and practices as they relate to contemporary Engineering practice
Relates to: Problem Solving Task - Analyse and evaluate Engineering problems using technical approaches informed by contemporary practice and leading edge research to achieve innovative, critically informed solutions
Relates to: Problem Solving Task - Apply innovative, systematic approaches to plan, design, deliver and manage Engineering projects in a way that assures sustainable outcomes over their whole lifecycle
Relates to: Water Modelling using AI - Effectively communicate Engineering problems, related complex data and information, and solutions in contemporary professional formats for diverse purposes and audiences
Relates to: Water Modelling using AI - Demonstrate ethically and socially responsible practice, recognising the importance of personal accountability and reflective practice when working in individual and collaborative modes
Relates to: Water Modelling using AI