Hydroinformatics Tools - 2023 entry
MODULE TITLE | Hydroinformatics Tools | CREDIT VALUE | 15 |
---|---|---|---|
MODULE CODE | ECMM124 | MODULE CONVENER | Prof Guangtao Fu (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
---|---|---|---|
DURATION: WEEKS | 12 weeks | 0 | 0 |
Number of Students Taking Module (anticipated) | 0 |
---|
Hydroinformatics (or water informatics) can be seen as a synergetic use of modelling tools and Information and Communication Technologies (ICT) within a single methodological approach dealing with physical, social and economic aspects of sustainable water management. This interdisciplinary field, which transcends traditional boundaries of water/environmental science and engineering, informatics/computer science (including Artificial Intelligence, data mining and optimisation techniques) and environmental engineering, has applications in various areas of water management, including:
- development and application of decision-support systems, simulation and optimization models to improve understanding and provide solutions to water engineering problems;
- computational tools and techniques and their effective application to managing risk and uncertainties associated with water systems;
- cross-disciplinary complex system approaches to water resource management;
- understanding of water systems, including technical, socio-economic and environmental issues.
On this module, you will improve your understanding of water systems (supply, drainage, flood management, structural/non-structural measures, risk management, their impact on social structures/interactions, etc), ICT and operations research techniques (simulation, optimisation, data mining/ machine learning, Geographic Information Systems, Bayesian Belief Networks, etc.) with a view of integrating them into a systems analytic context to analyse and solve problems in water resource design, planning and management practice.
By the end of this module, you should have a strong grasp of a number of Hydroinformatics tools and be able to develop or use the developed tools to model and optimise various water resource systems, as well as present your findings making sure the content is accurate, teaches the audience something, but in a way that is new, updated and technologically advanced.
This module aims to give you an advanced understanding of a number of data analytics and artificial intelligence technologies and their application to water management problems. It also offers practical experience in using these technologies within the urban water management context.
This module covers the topics of data analytics, water systems modelling and optimisation methods, using a problem-based learning approach in the case studies, and examining them through tutorials and assignments. You will develop independent learning skills through a combination of guided learning, background reading, private study and computational analysis.
On successful completion of this module, you should be able to:
Module Specific Skills and Knowledge:
1 understand the systems analysis approach to solving complex problems in water systems engineering;
2 comprehend a number of hydroinformatics methods and tools;
3 critically appraise the use of hydroinformatics methods and tools for a variety of water management problems.
Discipline Specific Skills and Knowledge:
4 be aware of physical, social and economic aspects of sustainable water management;
5 identify suitable methods and tools for water problem solving;
6 critically assess research results;
7 evidence some practical experience of using hydroinformatics methods and tools.
Personal and Key Transferable/ Employment Skills and Knowledge:
8 show enhanced independent learning;
9 demonstrate strong report and presentation skills;
10 reveal improved skills in using computer software.
- Systems approach to formulate water management problems and develop engineered solutions;
- Classical and intelligent optimisation strategies – linear programming and evolutionary computing;
- Data analytics and artificial intelligence methods – linear regression, artificial neural networks, Bayesian belief networks;
- Geographic Information System;
- Cellular automata and grid-based methods;
- System dynamics modelling;
- Application examples in water management: complex water network design, asset management, flood management.
Scheduled Learning & Teaching Activities | 48 | Guided Independent Study | 102 | Placement / Study Abroad |
---|
Category | Hours of study time | Description |
Scheduled learning activities | 48 | Lectures and tutorials |
Guided independent study | 102 | Assessment preparation; private study |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
---|---|---|---|
Questions posed and answered in the class | Various durations | All | Verbal (in class) |
Coursework | 30 | Written Exams | 70 | Practical Exams |
---|
Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
---|---|---|---|---|
Written exam | 70 | 2 hours - January Exam | All | Written (on request) |
Assignment on practical application of hydroinformatics tools | 30 | 40 hours | All | Written |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-reassessment |
---|---|---|---|
All above | Written exam (100% - 2 hours) | All | Referral/deferral period |
Reassessment will be by a single written exam only worth 100% of the module. For deferred candidates, the mark will be uncapped. For referred candidates, the mark will be capped at 50%.
information that you are expected to consult. Further guidance will be provided by the Module Convener
Web based and Electronic Resources:
Loucks, D P van Beek, E; Stedinger, J R; Dijkman, J P M; Villars, M . Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications.
Online (available through ELE) - UNESCO 2005 - 9231039989
Other Resources:
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN |
---|---|---|---|---|---|---|
Set | Pyle D | Data Preparation for Data Mining | Morgan Kaufmann | 1999 | 978-1558605299 | |
Set | Ross T J | Fuzzy Logic with Engineering Applications | 2nd | John Wiley | 2004 | 978-0470860755 |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
---|---|---|---|
PRE-REQUISITE MODULES | None |
---|---|
CO-REQUISITE MODULES | None |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
---|---|---|---|
ORIGIN DATE | Tuesday 10th July 2018 | LAST REVISION DATE | Thursday 5th October 2023 |
KEY WORDS SEARCH | Hydroinformatics; optimisation; modelling; machine learning; neural networks; genetic algorithms; cellular automata. |
---|
Please note that all modules are subject to change, please get in touch if you have any questions about this module.