Senior researcher (tenured)
Systems Transformation and Intelligent Network Operations, Department of Urban Water Management
Scopus author ID: 34870089600
My research involves the development of new methods to improve urban hydrology/ hydraulic modelling accuracy, taking advantage of newly available data resources: (i) identify urban water systems features (e.g. sewer inlets and manholes) from aerial imagery obtained using Unmanned Aerial Vehicles (UAVs), and (ii) estimate flow velocity and surface water depth from videos acquired from urban surveillance cameras and social media images, respectively.
I am also interested in the development of urban flood models to more realistically assess urban flood risk. This focus on the improvement of one-dimensional (1D) overland flow models and development of novel data-driven flood models. The ultimate goal is to investigate fast but accurate models targeted to be used in real-time flood forecasting applications.
In addition, I am also investigating Infrastructure Asset Management methods aiming at improving the industry’s perennial need for more efficient infrastructure, geared to reducing costs and risks while increasing its performance and flexibility.
Guo, Z., Moosavi, V., Leitão, J.P. (2022). Data-driven rapid flood prediction mapping with catchment generalizability. Journal of Hydrology, 609, 127726. doi: 10.1016/j.jhydrol.2022.127726
Harpaz, C., Russo, S., Leitão, J.P., Penn, R. (2022). Potential of supervised machine learn-ing algorithms for estimating the impact of water efficient scenarios on solids accumulation in sewers. Water Research, 216, 118247. doi: 10.1016/j.watres.2022.118247
Figueroa, A., Hadengue, B., Leitão, J.P., Rieckermann, J., Blumensaat, F. (2021). A distributed heat transfer model for thermal-hydraulic analyses in sewer networks. Water Research, 204, 117649. doi: 10.1016/j.watres.2021.117649
Wang, W., Leitão, J.P., Wani, O. (2021). Is flow control in space-constrained drainage networks effective? A performance assessment for combined sewer overflow reduction. Environmental Research, 111688. doi: 10.1016/j.envres.2021.111688
Jamali, B., Haghighat, E., Ignjatovic, A., Leitão, J.P., Deletić, A. (2021). Machine Learning for Accelerating 2D Flood Models: potential and challenges. Hydrological Processes, 35(4), e14064. doi: 10.1002/hyp.14064