FSE 5: Micro-climate modelling for efficient urban green space design

Faculty: Science and Engineering

Supervisors: Dr Lakshmi Babu Saheer, Dr Mahdi Maktabdar, and Dr Alison Greig

Interview date: 30 March 2022

This transdisciplinary project will address the problems of providing clean air for urban regions through effective green space design and sustainable human behavioural modelling. It integrates contributions of several disciplines such as computing (artificial intelligence), urban design and global sustainability.

The current surge of data generated by numerous internet of things (IoT) sensors and devices can be effectively used for developing creative urban planning and design solutions which address climate change and liveability in cities. The main aim of this project is to use data science and machine learning techniques to gather evidence, generate and understand the models for air quality and its relation to vegetation and social behavioural patterns. The outcome will help us propose planning strategies for a sustainable urban space to keep the carbon footprint under control.

Complex mathematical models are available for traffic, energy, air quality, health and wellbeing, vegetation, and nature conservation. However, these are not easily scalable to a new region/data. Recently, researchers have been trying to develop artificial intelligence (AI) or specifically machine learning (ML) models to replace these mathematical models and capitalise on the power of technology and big data.

Our previous research has proposed an efficient and cost-effective framework for air quality modelling. The successful candidate will utilise a holistic approach of data collection using existing custom devices as an IoT network and gathering datasets on road traffic, vegetation and weather around cities (Cambridge/Colchester) to model a sustainable predictive AI system. Performance will be evaluated through held out test sets, followed by live deployment and testing in collaboration with the city authorities.

The candidate will:

  • Collect air quality data, using this to develop and enhance micro-climate data models and policy generation frameworks to help design urban green spaces.
  • Analyse behavioural patterns of urban dwellers and incorporate findings into the model as mode of transportation and traffic patterns.
  • Test the effectiveness of the framework.

The successful candidate will be supervised by an experienced team with experience relevant to the topic. Dr Lakshmi Babu Saheer is an expert in AI for sustainability and has successfully analysed pollutant concentration data for London/Cambridge (Saheer, 2020) and, together with Dr Mahdi Maktabdar (expert in AI, IoT and computer vision), developed deep learning techniques to recognize number/species of trees from satellite imagery (Saheer, 2021).

Dr Alison Greig is an expert with experience in large European urban air quality projects and mathematical modelling. She is the Director of Education for Sustainability, with substantial experience in supervising students in transdisciplinary topics.

If you would like to discuss this research project prior to application please contact lakshmi.babu-saheer@aru.ac.uk

Apply online by 27 February 2022.

Funding notes

This successful applicant for this project will receive a Vice Chancellor’s PhD Scholarship which covers Home tuition fees and provides a UKRI equivalent minimum annual stipend for three years. For 2022/3 this will be £15,609 per year. The award is subject to the successful candidate meeting the scholarship terms and conditions. Please note that the University asserts the right to claim any intellectual property generated by research it funds.

Download the full terms and conditions.