Joao Paulo Leitao

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Dr. Joao Paulo Leitao

Senior scientist (Group Leader)

Department Urban Water Management

About Me

Senior researcher (tenured)

Systems Transformation and Intelligent Network Operations, Department of Urban Water Management

ResearcherID: F-5292-2012

Scopus author ID: 34870089600

ORCID: 0000-0002-7371-0543

Google Scholar

Research interests

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.

    Recent publications

    Peleg, N., Ban, N., Gibson, M.J., Chen, A.S., Paschalis, A., Burlando, P., Leitão, J.P. (2022). Mapping storm spatial profiles for flood impact assessments. Advances in Water Resources, 166, 104258. doi: 10.1016/j.advwatres.2022.104258

    Chaudhary, P., Leitão, J.P., D’Aronco, S., Perraudin, N., Obozinski, G., Perez-Cruz, F., Schindler, K., Wegner, J.D., Russo, S. (2022). Flood Uncertainty Estimation using Deep Ensembles. Water, 14, 2980. doi: 10.3390/w14192980

    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

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    Cost Effective Neural Technique to Alleviate Urban flood Risk
    Hexagonal Grids for urban flood modelling
    Alternative data collection and assimilation methods for urban flood modelling

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    Phone: +41 58 765 6714
    Fax: +41 58 765 5802
    Address: Eawag
    Überlandstrasse 133
    8600 Dübendorf
    Office: BU B09

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    Expert on

    GIS, urban planning, modeling, Risk assessment, urban water management

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    Urban flood modelling and assessment

    Image-based data sources for flood measurement

    Urban water infrastructure management 

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