Staff

Joao Paulo Leitao

Dr. Joao Paulo Leitao

Senior scientist (Group Leader)

Department Urban Water Management

About Me

Senior researcher (tenured)

Systems Engineering 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

    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

    Joshi, P., Leitão, J.P., Maurer, M., Bach, P.M. (2021). Not all SUDS are created equal: Impact of different approaches on Combined Sewer Overflows. Water Research, 191, 116780. doi: 10.1016/j.watres.2020.116780

    Guo, Z., Leitão, J.P., Simões, N.E., Moosavi, V. (2021). Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks. Journal of Flood Risk Management, 14(1), e12684. doi: 10.1111/jfr3.12684

    Chaudhary, P., D’Aronco, S., Leitão, J.P., Schindler, K., Wegner, J.D. (2020). Water level prediction from social media images with a multi-task ranking approach. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 252-262. doi: 10.1016/j.isprsjprs.2020.07.003

    Moy de Vitry, M., Leitão, J.P. (2020). The potential of proxy water level measurements for calibrating urban pluvial flood models. Water Research, 175, 115669. doi: 10.1016/j.watres.2020.115669

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    Projects

    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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    Address

    E-Mail: joaopaulo.leitao@eawag.ch
    Phone: +41 58 765 6714
    Fax: +41 58 765 5802
    Address: Eawag
    Überlandstrasse 133
    8600 Dübendorf
    Office: BU B09

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    Expert of

    urban water management, urban planning

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    Research Focus

    Urban flood modelling and assessment

    Image-based data sources for flood measurement

    Urban water infrastructure planning 

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