| Unit name | Modelling and Machine Learning for the Water Sector |
|---|---|
| Unit code | CADEM0007 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Laura Dickinson |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Basic knowledge of hydrology: Water Engineering (CENG20021) or Introduction to Hydrology Science (CADEM0004) or equivalent Basic knowledge of Python: Engineering by Investigation (MENG10005) or Introduction to Hydrology (CADEM0004) or equivalent |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Civil, Aerospace and Design Engineering |
| Faculty | Faculty of Engineering |
Why is this unit important?
Modelling and Machine Learning play an important role in addressing global water challenges. This unit will enable students to understand and employ cutting edge numerical modelling, data science and machine learning methods for wider applications in the water sector. This unit will cover the skills necessary to develop and evaluate models for applications to real-world water and environmental problems. This includes mechanistic modelling, data-driven and machine learning modelling and application of models for short and long-term prediction.
How does this unit fit into your programme of study?
This optional unit is part of the MSc in Water and Environmental Management (WEM) programme, but it can be taken by students from other programmes with an interest in modelling and machine learning applications to water. The WEM programme has been designed around five core subjects: fundamental hydrology, data science, environmental modelling, environmental management, and decision making.
This unit covers the topics of the environmental modelling subject and expands the hydrological processes covered in fundamental hydrology elsewhere, focusing here on modelling approaches - both mechanistic/process-based and Machine Learning/data-driven - and hands on application to a range of water and environmental prediction problems. The unit also allows the students to familiarize with relevant computing packages that can be used to build, validate and apply environmental models.
An overview of content
This unit will cover:
How will students, personally, be different as a result of the unit
You will learn about how engineers and water consultants apply computing techniques to real-world environmental problems. You will be able to calibrate and validate mechanistic and machine learning models for applications in the water sector.
Learning Outcomes
By the end of the unit, the students will be able to:
Your learning will be a combination of lectures and computer classes. This is to give you chance to learn about the concepts underpinning modelling methods, as well as an opportunity to apply the methods in practice to real-world problems and develop the ability to interpret results and their scope of validity.
Tasks which help you learn and prepare for summative tasks (formative):
You will be given computing tasks to complete throughout the unit. General feedback to your cohort will help you to prepare for the summative assessment.
Tasks which count towards your unit mark (summative):
The unit will be assessed by an individual report (100%) ILOs 1 and 3.
When assessment does not go to plan
The reassessment for the unit will be an individual report which tests all the unit learning outcomes.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. CADEM0007).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
