Engineering

ECMM124 - Hydroinformatics Tools (2010)

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MODULE TITLEHydroinformatics Tools CREDIT VALUE15
MODULE CODEECMM124 MODULE CONVENERProf Dragan Savic FREng (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS
Number of Students Taking Module (anticipated)
DESCRIPTION - summary of the module content
AIMS - intentions of the module
This module aims to provide a basic understanding of tools in the emerging field of hydroinformatics for the practising engineer. It also offers gaining practical experience in using these tools within the urban water management context.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
SYLLABUS PLAN - summary of the structure and academic content of the module
Week 1: Geographic Information Systems and visualization techniques; Weeks 2-3: Intelligent optimisation strategies - modern heuristic methods, evolutionary computing, simulated annealing, shuffled complex algorithm, particle swarm optimisation, ant colony systems; Week 4-5: Data mining methods, predictive data mining, knowledge discovery, rule-based methods, artificial neural networks, genetic programming, fuzzy sets; Week 6: Decision Support Systems (DSS), history, principles, frameworks, software tools, water management DSS; Week 7: Complexity - cellular automata and grid-based methods; Week 8: Modelling and models, typology, scale, forward and inverse modelling, calibration, validation and verification; Weeks 9-10: Application examples in urban water management: Calibration and validation; Data mining; Non-linear regression; Pattern recognition; Forecasting; Decision support.
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities Guided Independent Study Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
SUMMATIVE ASSESSMENT (% of credit)
Coursework 40 Written Exams 60 Practical Exams
DETAILS OF SUMMATIVE ASSESSMENT
DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
RE-ASSESSMENT NOTES
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Banzhaf W, Nordin P, Keller R E and Francone F D Genetic Programming: an introduction Morgan Kaufmann 1998 978-1558605107 [Library]
Set Haykin, S Neural Networks: A Comprehensive Foundation 2nd Pearson 1999 000-013-908-385-3 [Library]
Set Michaelewicz, Z Genetic Algorithms + Data Structures = Evolution Programmes 3rd Springer-Verlag 1996 000-354-055-387-8 [Library]
Set Pyle D Data Preparation for Data Mining Morgan Kaufmann 1999 978-1558605299 [Library]
Set Ross T J Fuzzy Logic with Engineering Applications 2nd John Wiley 2004 978-0470860755 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) M AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 15 December 2011 LAST REVISION DATE Thursday 15 December 2011
KEY WORDS SEARCH None Defined