Details
Aim
Floods are among the most destructive natural hazards, threatening ecosystems, urban areas, and critical infrastructure. In Scotland, the increasing frequency and intensity of floods driven by climate change, land-use dynamics, and human activity pose urgent challenges for urban resilience and disaster management.
This research aims to enhance flood hazard prediction in Scotland through intelligent spatial modeling, producing high-resolution flood susceptibility maps. By integrating machine learning algorithms both individual and hybrid with geospatial tools, the project will analyze a multivariate database encompassing hydrological, topographical, environmental, social, and economic factors.
The outcomes will deliver actionable insights for policymakers, urban planners, and communities, supporting proactive flood risk management, enhanced resilience, and sustainable infrastructure planning. This research also contributes to global efforts to adapt urban environments to climate change, positioning Scotland as a model for climate-resilient development.
Research Objectives
The PhD will focus on:
- Mapping and Classifying Flood-Susceptible Urban Areas: Develop detailed archetypes of urban river basins in Scotland using GIS data.
- High-Resolution Flood Hazard Prediction: Leverage smart data collection, monitoring, and AI algorithms to model flood susceptibility under varying climatic scenarios.
- AI Modelling for Urban Resilience: Combine multiple machine learning approaches to refine predictive accuracy and identify key drivers of flood risk.
- Data Driven Policy Guidelines for Climate-Resilient Infrastructure: Translate research findings into actionable strategies for urban planning, community adaptation, and sustainable infrastructure development.
Expected Outcomes
- Innovative AI models for predicting complex flood hazards.
- A comprehensive spatial decision-support framework for flood risk management in Scotland.
- Transferable methodology for other UK and European regions, contributing to global climate resilience research.
- Evidence-based recommendations for policymakers, urban planners, and local communities to enhance flood preparedness and adaptive capacity.
Why This Research Matters
- Flooding in Scotland has caused substantial human, economic, and environmental impacts over the past two decades. Traditional modeling approaches often fail to capture the complexity of flood hazards, while AI-driven spatial analysis remains underutilized in the region. This project addresses this gap by providing advanced tools to anticipate, adapt, and respond to future flood risks, supporting decision-making at local, regional, and national levels.
- Scotland is increasingly experiencing severe flooding events, threatening urban infrastructure, communities, and ecosystems. This PhD offers a unique opportunity to conduct cutting-edge research at the intersection of climate science, urban resilience, artificial intelligence, and geospatial analysis, developing innovative solutions for flood risk prediction and climate-resilient infrastructure planning.
Why Join This PhD?
- Work at the forefront of AI, GIS, and climate resilience research.
- Contribute directly to real-world solutions for urban flooding in Scotland.
- Collaborate with a dynamic team of experts across geoscience, urban planning, and environmental management.
Academic qualifications
A first degree (at least a 2.2) ideally in Geography/Geospatial Science, Civil/Environmental/Urban Engineering, Environmental Science/Management and Earth Sciences.
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
Essential attributes:
- Strong fundamentals in: Geospatial Analysis & GIS: Spatial data collection, mapping, and remote sensing.
- Environmental Science: Flood processes, climate impacts, and socio-environmental factors.
- Data Science & AI: Machine learning basics, predictive modeling, and hybrid approaches.
- Urban Infrastructure & Planning: Urban systems, land-use impacts, and resilience principles.
- Programming & Computational Skills: Python/R, geospatial libraries (ArcGIS, QGIS, Google Earth Engine).
- Strong analytical and problem-solving skills.
- Proficiency in Python/R, machine learning, and GIS/spatial analysis.
- Knowledge of flood processes, urban infrastructure, and climate impacts.
- Ability to work independently and manage complex research projects.
- Excellent communication and teamwork skills for multidisciplinary collaboration.
- Commitment to applying research for climate resilience and sustainable urban planning.
Desirable:
- Secondary / Relevant Backgrounds considered: Computer Science / Data Science / Artificial Intelligence / Machine Learning – Experience in coding, AI, or data-driven modeling.
When applying click here
APPLICATION CHECKLIST
- Completed application form
- CV
- 2 academic references, using the Postgraduate Educational Reference Form (download)
- Research project outline of 2 pages (list of references excluded). The outline may provide details about:
- Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
- Research questions or objectives.
- Methodology: types of data to be used, approach to data collection, and data analysis methods.
- List of references.
The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
- Statement no longer than 1 page describing your motivations and fit with the project.
- Evidence of proficiency in English (if appropriate)
To be considered, the application must use
- the advertised title as project title
For informal enquiries about this PhD project, please contact Dr Lina Khaddour – l.khaddour@napier.ac.uk
https://www.napier.ac.uk/research-and-innovation/doctoral-college/application-guidance
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