MABY5350 Sensor Networks and Model Based Decisions Support Emneplan

The course will give an understanding of the status and potential for the use of sensors and models for decision support and system diagnostics in urban water systems. Since each sensor only measures in a single location, we need models to interpret the sensor data and give us an idea about what is happening throughout the system. The combination of models and sensor data will also be used for forecasting purposes and to increase data security by estimating if sensor data is realistic or not. The student will learn about the types of sensor data available now and in the foreseeable future and how to utilize the data for decision support and diagnostic purposes. The student will learn how to do model-based inference to learn about the state of a system by combining models and sensor data. The students will be exposed to numerous real-life cases. The course will cover the following key aspects: Simple conceptual modelling, Bayesian inference, Ensemble-based system diagnostics, forecasting, Real time control, data in water distribution systems, opportunities and challenges for smart control of urban water systems.

The students will make use of software such as Matlab and Python.

Grade scale A-F

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student will have

• broad knowledge about the various sensor types used within the different fields of urban water;
• deep knowledge about the most common properties, deficiencies and uncertainties for the various sensor types;
• insight into the current state-of-the-art of sensor and model usage in current urban water management;
• good understanding of the different requirements for models for design, diagnostics and forecasting.

Skills

The student

• can design and implement simple hydrological/hydraulic models;
• can interpret data using a model;
• can use a model to critically assess sensor data and balance conflicting data against each other for systemwide diagnostics.

General competences

The student will know how to

• integrate knowledge into practice in the water industry to apply this to a range of scenarios, to critically analyse, evaluate, interpret and report on information from a range of sources and to solve complex problems systematically and creatively;
• critically assess sensor data.

The teaching will consist of a combination of:

Lectures & discussions

Independent studies including video recordings and online exercises

Coursework assignments

Guest lectures

Practical use of tools and software.

Two individual multiple-choice of 30 minutes each must be passed.

This course aims to provide students with an understanding of the complexities involved with infrastructure projects and the tools required for delivering the project and its wider benefits originally envisaged - on time, on budget, and within scope. It also covers a wide range of skill sets and knowledge areas needed during the life cycle of infrastructure projects in order to ensure successful delivery and at the same time enable them to make decisions and take actions based on the best interest of society, public safety, and the environment.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge:

The student:

• has sufficiently advanced knowledge of large complex project life cycle (From feasibility to closure) and the complexities involved with managing such projects to be able to develop and propose innovative solutions to potential project problems.
• is capable of identifying and analyzing project risks and possibilities in addition to developing suitable responses to potential risks.
• has advanced insight into the contracting and procurement process for infrastructure projects.
• has specialized knowledge regarding the societal and environmental values and impacts of infrastructure projects.

Skills:

The student is capable of:

• analytically criticizing and justifying the various project concepts and objectives to the wider stakeholder group.
• demonstrating a clear understanding of the governance framework, and aligning the project objectives with the higher-level strategic objectives.
• explaining relevant regulatory requirements and policies under which the project operates.
• handling the complex cultural aspects of the project supply network.
• demonstrating excellent managerial capabilities aimed at effectively managing project performance
• demonstrating an understanding of the importance of leadership qualities and organizational culture awareness in large complex project settings.

General competence:

The student is capable of:

• using scholarly articles and published case studies to keep up with the latest developments in the field.
• working in teams and managing their own and their team's workload.
• presenting results in a scholarly, professional manner with the help of written reports and oral presentations.
• communicating internally within the organization and externally with the society and participating in evidence-based public debate on the project outcomes and potential benefits.

The students must have individual reflection notes (approx. 4-5 pages per note) approved and linked to 80% of the total guest lectures. Students who fail to meet the coursework requirements can be given up to one opportunity to resubmit reflection notes before the exam.

The assessment consists of two parts:

Part 1) Project report prepared individually or in a group of max. 3 students, approx. 20-30 pages, weighted 70 %.

Part 2) Oral examination or presentation individually 30 %.

All assessment parts must be awarded a pass grade (E or better) in order for the student to pass the course.

Assessment parts: 1) can be appealed, 2) cannot be appealed