Integrative decision support system for flood management

Flooding is a major hydrological threat that impacts more than 1.81 billion people (23% of world population) in the world (Rentschler 2022). Its frequency and severity are rising due to urbanisation and climate change. The preparation and response to major flood events are complex activities involving various stakeholders at different levels of decision making. Effective and efficient decision making in a major flood event could mean the difference between life and death. Even though there is a shift in system thinking approach in flood risk management (Awah 2024), current decision support systems treat different phases and agents involved in flood management separately, causing inefficiencies in the entire process (Wang 2022). Taking the relief items as an example, the location and storage of relief items are usually decided in a strategic or tactic level, in the preparation phase of the flood event, while the routing of relief items is usually involved in an operational level in the response phase of the event. “Bad” decisions made for location and storage of relief items prior to a flood disaster can make the routing of the items during the disaster inefficient, costing time, money and even lives. On the other hand, since flood events are very unpredictable, the decisions made in different phases must be robust in different scenarios. It is thus highly important to design an integrative decision support system, capable of analysing various decisions in different scenarios simultaneously, to make sure that relief items can be allocated to people in need in the most efficient way. Recent technological development in artificial intelligence and operational research allows the development of more integrative decision support system involving decisions made in different stage of flood event by various stakeholders.

This PhD project aims to create a prototype of this integrative decision support system for the location, storage and routing of relief items in both preparation and response phase of a flood event.

The candidate is expected to:

* analyse existing related literature to have a comprehensive understanding of the issue

* take historical data of flood events in selected regions in the UK to build models to predict risks of flood

* perform a comparative analysis of various flood scenarios

* build optimisation models to support integrative decision making for flood management

* explore solution methods integrating mathematical programming techniques, fuzzy optimisation, and machine learning analysis

The candidate will join a vibrant and friendly group of researchers in the Sustainable Operations, Optimisation and Analytics Research Lab in Nottingham Business School and benefit from professional advice and guidance of the supervisory team.

Nottingham Business School is triple crown accredited with EQUIS, AACSB and AMBA – the highest international benchmarks for business education. It has also been ranked by the Financial Times for its Executive Education programmes in 2023 and 2024. NBS is one of only 47 global business schools recognised as a PRME Champion, and held up as an exemplar by the United Nations of Principles of Responsible Management Education (PRME). 

Its purpose is to provide research and education that combines academic excellence with positive impact on people, business and society. As a world leader in experiential learning and personalisation, joining NBS as a researcher is an opportunity to achieve your potential.

Our PhDs have four intakes per year and Professional Doctorates have two. For more information on when and how to apply, see here.