category_student_information
MSc thesis topic: Optimizing sustainability in urban natural resource flows
Urbanization has led to increased demands for energy, water, minerals, and other natural resources. Cities rely heavily on resource imports to meet the needs of their growing populations. However, this dependence comes with environmental consequences, including pollution, habitat destruction, and competition for land and water. Moreover, the linear nature of resource flows—where resources are extracted, used, and discarded—poses significant challenges. The efficient management of natural resource flows within urban contexts is critical for achieving sustainability goals. This topic delves into understanding how cities consume and manage resources, addressing challenges related to resource scarcity, pollution, and equitable distribution. By optimizing resource flows, we can pave the way for a more sustainable urban future.
Currently, resource management often operates in silos, with different sectors pursuing distinct objectives. However, this fragmented approach overlooks the intricate relationships among resources and their cumulative impact on urban sustainability. To tackle these complexities, spatial multi-objective optimization techniques emerge as a powerful tool.
By integrating optimization models, such as genetic algorithms, with urban planning, we can explore trade-offs and generate optimal resource allocation plans. The aim is to achieve a harmonious balance among conflicting objectives while adhering to constraints such as land availability and environmental regulations.
Objectives and Research questions
To develop an integrated approach for spatially-enabled optimization of urban natural resource flows
- What are the key factors influencing resource allocation decisions in urban planning, and how can they be integrated into a spatially-enabled multi-objective optimization framework?
- How do different land use patterns impact resource utilization and environmental quality?
- What trade-offs exist between conflicting objectives (e.g., economic growth vs. environmental conservation) when optimizing resource flows?
- How can we incorporate stakeholder preferences and community engagement into the optimization process to ensure equitable resource distribution?
Requirements
- GIS proficiency: Familiarity with GIS is essential. You’ll work with spatial data layers, perform analyses, and create maps.
- Python basics: Basic knowledge of Python is required. Some multi-objective optimization models and algorithms have already been developed, and you’ll need to adapt and extend them.
- Willingness to learn multi-objective optimization: While prior knowledge of multi-objective optimization models is beneficial, a willingness to learn and explore the principles is essential.
Literature and information
- Zijing Liu, Zhaoxing Han, Xiaoyu Shi, Xinyu Liao, Linyuan Leng, Haifeng Jia, Multi-objective optimization methodology for green-gray coupled runoff control infrastructure adapting spatial heterogeneity of natural endowment and urban development, Water Research, 2023.
- Ghodsvali, M., Dane, G., de Vries, B., An integrated decision support system for the urban food-water-energy nexus: Methodology, modification, and model formulation, Computers, Environment and Urban Systems, 2023.
Theme(s): Modelling & visualisation, Human – space interaction