Dr. Katayoun Kargar | Urban Drainage Systems | Research Excellence Award

Toronto Metropolitan University | Canada

Dr. Katayoun Kargar is a civil engineering researcher whose work advances the understanding, modelling, and management of sewer blockages within urban drainage networks, particularly those caused by non-flushable consumer products. Her research integrates hydraulic modelling, data analytics, and infrastructure asset management to address performance deterioration in sewer systems and provide actionable strategies for municipalities. She has contributed original methodologies for analyzing blockage mechanisms using open datasets, quantified the hydraulic impacts of wipe-induced obstructions, and developed simulation frameworks capable of capturing flow alterations and risk conditions in sewer networks. Her work with SWMM includes creating and validating procedures that allow utilities to incorporate blockage dynamics into routine modelling, enabling more accurate capacity evaluations, maintenance prioritization, and system reliability assessments. By translating laboratory insights into practical planning tools, she supports municipalities in enhancing inspection regimes, optimizing rehabilitation strategies, and strengthening long-term infrastructure resilience. Her scholarly contributions include publications indexed in major scientific databases and presentations at leading professional forums, where her work has been recognized through notable awards for research excellence and technical communication. Complementing her academic achievements, she has professional experience in asset management consulting, applying modelling outputs and analytical approaches to assist municipalities in developing asset management plans, capital planning frameworks, and infrastructure lifecycle strategies. Her combined research and practical expertise contribute to advancing municipal wastewater infrastructure management and promoting data-informed decision-making in the engineering of sustainable urban drainage systems.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Shamshirband, S., Hashemi, S., Salimi, H., Samadianfard, S., Asadi, E., … (2020). Predicting standardized streamflow index for hydrological drought using machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 339–350.

Samadianfard, S., Hashemi, S., Kargar, K., Izadyar, M., Mostafaeipour, A., … (2020). Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Reports, 6, 1147–1159.

Kargar, K., Samadianfard, S., Parsa, J., Nabipour, N., Shamshirband, S., … (2020). Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms. Engineering Applications of Computational Fluid Mechanics, 14(1), 311–322.

Kargar, K., Safari, M. J. S., Mohammadi, M., Samadianfard, S. (2019). Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques. Water Science and Technology, 79(12), 2318–2327.

Safari, M. J. S., Mohammadi, B., Kargar, K. (2020). Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit. Journal of Cleaner Production, 276, 124267.