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Data-driven rapid flood prediction mapping with catchment generalizability
Guo, Z., Moosavi, V., & Leitão, J. P. (2022). Data-driven rapid flood prediction mapping with catchment generalizability. Journal of Hydrology, 609, 127726 (12 pp.). https://doi.org/10.1016/j.jhydrol.2022.127726
Potential of supervised machine learning algorithms for estimating the impact of water efficient scenarios on solids accumulation in sewers
Harpaz, C., Russo, S., Leitão, J. P., & Penn, R. (2022). Potential of supervised machine learning algorithms for estimating the impact of water efficient scenarios on solids accumulation in sewers. Water Research, 216, 118247 (16 pp.). https://doi.org/10.1016/j.watres.2022.118247
Stormwater management impacts of small urbanising towns: The necessity of investigating the 'devil in the detail'
Browne, S., Lintern, A., Jamali, B., Leitão, J. P., & Bach, P. M. (2021). Stormwater management impacts of small urbanising towns: The necessity of investigating the 'devil in the detail'. Science of the Total Environment, 757, 143835 (13 pp.). https://doi.org/10.1016/j.scitotenv.2020.143835
A distributed heat transfer model for thermal-hydraulic analyses in sewer networks
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 (11 pp.). https://doi.org/10.1016/j.watres.2021.117649
Data-driven flood emulation: speeding up urban flood predictions by deep convolutional neural networks
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 (14 pp.). https://doi.org/10.1111/jfr3.12684
Machine learning for accelerating 2D flood models: potential and challenges
Jamali, B., Haghighat, E., Ignjatovic, A., Leitão, J. P., & Deletic, A. (2021). Machine learning for accelerating 2D flood models: potential and challenges. Hydrological Processes, 35(4), e14064 (14 pp.). https://doi.org/10.1002/hyp.14064
Not all SuDS are created equal: impact of different approaches on combined sewer overflows
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 (13 pp.). https://doi.org/10.1016/j.watres.2020.116780
The new town of Angra (Terceira, the Azores): confirming a contested urban planning history using reverse historical analysis and flood modelling tools
Leite, A., & Leitão, J. (2021). The new town of Angra (Terceira, the Azores): confirming a contested urban planning history using reverse historical analysis and flood modelling tools. Urban History, 48(1), 20-36. https://doi.org/10.1017/S0963926819001093
Is flow control in a space-constrained drainage network effective? A performance assessment for combined sewer overflow reduction
Wang, W., Leitão, J. P., & Wani, O. (2021). Is flow control in a space-constrained drainage network effective? A performance assessment for combined sewer overflow reduction. Environmental Research, 202, 111688 (11 pp.). https://doi.org/10.1016/j.envres.2021.111688
Water level prediction from social media images with a multi-task ranking approach
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. https://doi.org/10.1016/j.isprsjprs.2020.07.003
Analysis of effect of rainfall patterns on urban flood process by coupled hydrological and hydrodynamic modeling
Cheng, T., Xu, Z., Yang, H., Hong, S., & Leitao, J. P. (2020). Analysis of effect of rainfall patterns on urban flood process by coupled hydrological and hydrodynamic modeling. Journal of Hydrologic Engineering, 25(1), 04019061 (14 pp.). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001867
The potential of proxy water level measurements for calibrating urban pluvial flood models
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 (14 pp.). https://doi.org/10.1016/j.watres.2020.115669
How urban storm- and wastewater management prepares for emerging opportunities and threats: digital transformation, ubiquitous sensing, new data sources, and beyond – a horizon scan
Blumensaat, F., Leitão, J. P., Ort, C., Rieckermann, J., Scheidegger, A., Vanrolleghem, P. A., & Villez, K. (2019). How urban storm- and wastewater management prepares for emerging opportunities and threats: digital transformation, ubiquitous sensing, new data sources, and beyond – a horizon scan. Environmental Science and Technology, 53(15), 8488-8498. https://doi.org/10.1021/acs.est.8b06481
Automated localization of urban drainage infrastructure from public-access street-level images
Boller, D., Moy de Vitry, M., Wegner, J. D., & Leitão, J. P. (2019). Automated localization of urban drainage infrastructure from public-access street-level images. Urban Water Journal, 16(7), 480-493. https://doi.org/10.1080/1573062X.2019.1687743
Flood-water level estimation from social media images
Chaudhary, P., D'Aronco, S., Moy de Vitry, M., Leitão, J. P., & Wegner, J. D. (2019). Flood-water level estimation from social media images. In G. Vosselman, S. J. Oude Elberink, & M. Y. Yang (Eds.), ISPRS annals of the photogrammetry, remote sensing and spatial information sciences: Vol. IV-2/W5. ISPRS geospatial week (pp. 5-12). https://doi.org/10.5194/isprs-annals-IV-2-W5-5-2019
Public surveillance and the future of urban pluvial flood modelling
Moy de Vitry, M. (2019). Public surveillance and the future of urban pluvial flood modelling [Doctoral dissertation, ETH Zurich]. https://doi.org/10.3929/ethz-b-000397587
Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network
Moy de Vitry, M., Kramer, S., Wegner, J. D., & Leitão, J. P. (2019). Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network. Hydrology and Earth System Sciences, 23(11), 4621-4634. https://doi.org/10.5194/hess-23-4621-2019
Smart urban water systems: what could possibly go wrong?
Moy de Vitry, M., Schneider, M. Y., Wani, O., Manny, L., Leitão, J. P., & Eggimann, S. (2019). Smart urban water systems: what could possibly go wrong? Environmental Research Letters, 14(8), 081001 (4 pp.). https://doi.org/10.1088/1748-9326/ab3761
Generalizing multi-reward functions aimed at identifying the best locations to install flow control devices in sewer systems
Muñoz, D. F., Simões, N. E., de Sousa, L. M., Maluf, L., Sá Marques, A., & Leitão, J. P. (2019). Generalizing multi-reward functions aimed at identifying the best locations to install flow control devices in sewer systems. Urban Water Journal, 16(8), 564-574. https://doi.org/10.1080/1573062X.2019.1700284
Abflussmessungen mittels Videos. Einsatz von Webcams und Smartphones
Peña-Haro, S., Carrel, M., Lüthi, B., Wang, L., Dicht, S., & Leitão, J. P. (2019). Abflussmessungen mittels Videos. Einsatz von Webcams und Smartphones. Aqua & Gas, 99(10), 42-45.