ENV ENG 7206 - Climate & Environmental Impact Modelling
North Terrace Campus - Semester 2 - 2024
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General Course Information
Course Details
Course Code ENV ENG 7206 Course Climate & Environmental Impact Modelling Coordinating Unit Environmental Engineering Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange N Incompatible C&ENVENG 4087, C&ENVENG 3029, CEME 2006 or ENV ENG 2006 Assumed Knowledge ENG 1003, CEME 1001 or ENV ENG 1001 or equivalent Assessment Tests/quizzes, assignments/projects Course Staff
Course Coordinator: Professor Holger Maier
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course students will be able to:1. Recognise, discuss, apply, test and critically evaluate different model types (e.g. data-driven (machine learning), process-driven).2. Recognise, discuss, apply, test and critically evaluate the different steps in the development of models (e.g. model specification, calibration and validation) and the methods used in each of these steps.3. Develop, test and apply process-driven dissolved oxygen and data-driven (machine learning) salinity models in river systems.4. Distinguish between sources and different types of uncertainty, explain their potential origins and discuss how they might impact engineering modelling and decision-making.5. Recognise, interpret, discuss, apply, test and critically evaluate different approaches to incorporating uncertainty into engineering modelling and decision-making.6. Use models and multi-criteria decision analysis approaches to solve complex engineering problems that examine the trade-offs between economic, environmental and social outcomes in an uncertain environment,including the development of solutions to adapt to climate change impacts.7. Describe, discuss and critically evaluate modelling and management processes, findings and decisions.8. Apply an integrative or systems approach to solving engineering problems.9. Use computers and information technology effectively.
The above course learning outcomes are aligned with the Engineers 最新糖心Vlog . The course develops the following EA Elements of Competency to levels of introductory (A), intermediate (B), advanced (C):
1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 3.6 B B C — A — B B B A — A — — A A 最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog Graduate Attribute Course Learning Outcome(s) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1-9 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
2,3,5,6,8 Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
7 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
8,9 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
7,8 -
Learning Resources
Required Resources
All required learning resources are made available online, which include interactive online modules that cover all relevant theory and information on how to complete the project. Students will need a computer and Microsoft Excel to complete the modelling component of the course. All modelling spreadsheets are provided.Recommended Resources
All recommended learning resources will be provided on MyUni.Online Learning
All required learning resources are made available online, which include interactive online modules that cover all relevant theory and information on how to complete the design project. -
Learning & Teaching Activities
Learning & Teaching Modes
This course utilises a blended learning approach, consisting of a combination of interactive online activities and face-to-face design sessions. However, while the face-to-face design sessions are likely to enhance learning, the course is able to be completed successfully in online mode only.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
The total expected workload for this course is 150 hours.Learning Activities Summary
This course consists of a set of integrated learning activities designed to provide you with the opportunity to achieve the course learning outcomes in a supportive and motivating context. The centrepiece of the course is a Design Project that consists of three stages and spans the duration of the entire course. You will learn the fundamental principles / underlying the theory required to complete the project via a series of online learning units, consisting of interactive online modules. The Design Project provides you with the opportunity to gain an in-depth understanding of the material covered by applying it in a realistic context.
In additionl to a weekly Q&A session, learning activities include the following:
Online Learning Units – Theory:
Week 1: Environmental Management, Model Specification (Process-Driven Models, Model Complexity)
Week 2: Model Specification (Dissolved Oxygen Modelling)
Week 3: Model Calibration
Week 4: Model Validation
Week 5: Specification and Calibration of Artificial Neural Network (ANN) Models
Week 6: Evolutionary Algorithms
Week 7: Validation of ANN Models
Week 9: Local Uncertainty. Global Uncertainty
Week 10: Identification of Optimal Solutions
Week 11: Multi Criteria Decision Analysis
Online Learning Units – Project:
Week 1: Project Overview. Stage 1 – Background and Software
Week 2: Industry Guest Lecture – Dissolved Oxygen Modelling; Stage 1 – Task 1
Week 3: Stage 1 – Task 2
Week 4: Stage 1 – Task 3
Week 5: Stage 2 – Background and Software; Industry Guest Lecture – Salinity Modelling
Week 6: Stage 2 – Task 1; Stage 2 – Task 2
Week 7: Stage 2 – Task 3
Week 8: Stage 2 – Task 3
Week 9: Stage 3 – Background; Stage 3 – Tasks 1 to 3
Week 10: Stage 3 – Tasks 1 to 3
Week 11: Stage 3 – Task 4
Week 12: Stage 3 – Task 5
Project Sessions (Face-to-Face):
Week 1: Project Familiarisation
Week 2: Specification of Streeter-Phelps Dissolved Oxygen Model
Week 3: Calibration of Streeter-Phelps Dissolved Oxygen Model
Week 4: Validation of Streeter-Phelps Dissolved Oxygen Model
Week 5: Familiarisation with Stage 2
Week 6: Specification and Calibration of Artificial Neural Network Salinity Model
Week 7: Calibration and Validation of Artificial Neural Network Salinity Model
Week 8: Validation of Artificial Neural Network Salinity Model; Comparison of Modelling Approaches (Stages 1 and 2)
Week 9: Familiarisation with Stage 3; Assessment of Impact of Climate and Population Change
Week 10: Familiarisation with Stage 3; Assessment of Impact of Climate and Population Change
Week 11: Development of Most Appropriate Mitigation Strategy
Week 12: Development of Most Appropriate Mitigation Strategy -
Assessment
The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
A summary of assessment tasks and weightings are given below. All assessment tasks are individual. There is no exam for this course.
Online Quizzes: 10%
Design Project (Stage 1): 27%
Design Project (Stage 2): 27%
Design Project (Stage 3): 27%
Test: 9%Assessment Related Requirements
Rationale:
The assessment tasks have been developed to ensure that all course learning outcomes are being assessed. The assessment tasks represent a mix of assessment types in order to maximise opportunities for individuals to demonstrate their knowledge of the course material in relation to the learning objectives. Some assessment tasks, such as the online quizzes, are designed to provide students with the opportunity to test their knowledge and understanding of basic facts and principles and are primarily formative, with the added advantage of instant feedback. Other assessment tasks are more open ended and require a deeper understanding of the underlying concepts and principles and build on the knowledge gained through completion of the online quizzes. While these projects are summative in the sense that the mark for the final report counts towards a significant portion of the final course mark, the process of completing the tasks will result in significant learning and has therefore some formative elements to it
Extensions and Late Submissions:
Students seeking extensions for submissions should do so in accordance with the Modified arrangements for coursework assessment policy. Late submissions will be accepted, but there will be a 10% reduction in marks for every day (i.e. if the submission is up to 24h late, there will be a 10% reduction in marks; if the submission is between 24 and 48h late, there will be a 20% reduction in marks etc.).
Feedback:
The feedback schedule has been devised to strike an appropriate balance between response time and the amount of detail provided. For online tasks, details will be provided in relation to where and how many marks have been lost (marked on the submissions). In relation to the tasks for which detailed assessment criteria have been provided, written feedback will be given against these criteria via the use of the assessment rubrics provided.Assessment Detail
Online Quizzes (10%)
Week 1: Environmental Management; Specification of Process-Driven Models; Model Complexity
Week 2: Dissolved Oxygen Modelling in Rivers
Week 3: Model Calibration
Week 4: Model Validation
Week 5: Specification and Calibration of Artificial Neural Network Models
Week 6: Genetic Algorithms
Week 7: Validation of Artificial Neural Network Models
Week 9: Local Uncertainty; Climate Change
Week 10: Identification of Optimal Solutions
Week 11: Multi-Criteria Decision Analysis
Project:
Students will work on a project for the duration of the course, which is split into three stages of four weeks’ duration. As part of the project, students will have the opportunity to develop a process-driven dissolved oxygen and a data-driven / machine learning (artificial neural network) salinity model for a river, covering all stages of the model development process, including model specification, model calibration and model validation. In addition, students will utilise these models to perform a climate change impact assessment in accordance with a number of risk-based performance criteria and to determine optimal climate change adaptation strategies for the river considering competing environmental and economic objectives.
Stage 1 - Development of Process-Driven Dissolved Oxygen Model (Weeks 1-4, 27%)
- Task 1: Check Specification of Existing Dissolved Oxygen Model
- Task 2: Calibrate Dissolved Oxygen Model
- Task 3: Validate the Calibrated Dissolved Oxygen Model
Stage 2 - Development of Data-Driven Artificial Neural Network Salinity Model (Weeks 5-8, 27%)
- Task 1: Check Numerical Specification of Artificial Neural Network Salinity Model
- Task 2: Calibrate / Determine Optimal Structure of Artificial Neural Network Salinity Model
- Task 3: Validate the Calibrated Artificial Neural Network Salinity Model
- Task 4: Compare Stage 1 and 2 Model Development Processes
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Stage 3 – Climate / Population Change Impact Assessment and Development of Optimum Mitigation Strategies (Weeks 9-12, 27%)
Assessment of Impact of Climate and Population Change
- Task 1: Assessment of Current Impact
- Tasks 2 and 3: Assessment of Future Impact
Development of Most Appropriate Mitigation Strategy
- Task 4: Identify Management Strategies that Provide Optimal Trade-Offs
- Task 5: Identify Preferred Management Strategies
Test (9%):
Students will take a test on the following material, which is not assessed in any of the other assessent tasks:
- Determination of inputs for Artificial Neural Network models
- Ant Colony Optimisation
- First Order Reliability Method
- Decision Making Under Deep Uncertainty
- Uncertainty in Multi-Criteria Decision AnalysisSubmission
Online quizzes are completed online, with instant feedback provided.
The project submissions for each of the three stages consist of the following and are submitted online:
- A written submission using a character-limited word template provided
- Supporting material
- Calculation spreadsheetCourse Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The 最新糖心Vlog places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the 最新糖心Vlog to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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