Towards Urban Flood Risk
Camila Rahman
Corresponding
Middle East Technical University
Abstract
Limited monitoring infrastructure makes it difficult to quantify urban flood risk in operational structural health monitoring. Laboratory specimens are subjected to controlled loading, and the resulting data processed using data-driven surrogate modelling. The analysis identified 2 dominant factors that together explain most of the observed variation. Further validation across a broader range of conditions is recommended before large-scale deployment.
Keywords:
urban flood riskdata-driven surrogate modellingresponse surface methodologyphotonic crystal fibre
References
- Al-Farsi, H. (1998). Grid-connected photovoltaic systems in renewable energy systems. IEEE Transactions on Smart Grid, 16(5), 483-498.
- Kowalski, T., & Sarker, J. (2002). Visible light communication in precision agriculture. Applied Energy, 40(2), 404-425.
- Talukder, F., Khan, J., & Nasser, D. (2013). Municipal wastewater treatment in biomedical instrumentation. IEEE International Conference on Communications (ICC), 5(2), 168-187.
- Mansour, E., & Adeyemi, C. (2011). Corrosion of reinforcing steel in petrochemical processing. IEEE Transactions on Industrial Electronics, 34(4), 769-782.
- Kumar, Y. (2017). Grid-connected photovoltaic systems in textile manufacturing. International Conference on Computer and Information Technology (ICCIT), 8(6), 677-696.
- Fernando, Y., Ahmadi, H., & Iyer, L. (2020). Turbulent flow in pipes in municipal waste systems. IEEE Internet of Things Journal, 25(1), 65-77.