A Review of Urban Flooding Management Approaches: Predictive Modeling for Stormwater Catch Basins
Roberto ROSARIO, Sean Padolina PADOLINA, Meriam LEOPOLDO, Razon DOMINGO
Abstract. Urban flooding has become a major global problem that is caused by the fast growth of cities, climate change, and the lack of proper infrastructure to cope with the increased stormwater runoff. Therefore, an effective stormwater management system is necessary to alleviate flooding by gathering and rerouting rainwater to the drainage systems. Predictive modeling has turned into an important tool for stormwater management simulation, as it can demonstrate runoff behavior and the impact of catch basins. Different hydrologic and hydraulic modeling software each have their own features for studies related to stormwater, watershed, and floodplains. The EPA SWMM integrates green solutions, such as rainwater harvesting, bio-retention systems, permeable pavements, floodplains, and infiltration trenches. The PCSWMM makes it possible to predict floods in real time and design detention systems making it appropriate for large and multiple uses watersheds at the same time. HEC-HMS provides a solid base for event-based and watershed simulations that require a lot of data sources and different runoff diversion methods. Meanwhile, InfraWorks and XPSWMM are dedicated to integrated hydrologic-hydraulic modeling. Moreover, MIKE+ and OpenFlows FLOOD provide high resolution detailed flood simulations and sophisticated unified platforms. Nevertheless, there is not a single modeling platform that completely solves the problem of hydrological, hydraulic and urban variability. The review applied Latent Dirichlet Allocation (LDA) to select relevant papers from various databases and identify the research issues in the simulation software for urban flooding management, which in turn led to the future research and policy making in flood control systems. The research shows that existing modeling tools can only partially connect real-time monitoring information with climate changes and machine learning methods to create complete prediction systems. The research results give new understanding about creating adaptive data-driven flood modeling methods which will help improve stormwater management techniques and upcoming flood management policies for urban areas at risk of flooding.
Keywords
Stormwater Management, Urban Flooding, Machine Learning, Predictive Modeling, Flood Control System, Sustainable Water Management
Published online 5/10/2026, 16 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Roberto ROSARIO, Sean Padolina PADOLINA, Meriam LEOPOLDO, Razon DOMINGO, A Review of Urban Flooding Management Approaches: Predictive Modeling for Stormwater Catch Basins, Materials Research Proceedings, Vol. 66, pp 390-405, 2026
DOI: https://doi.org/10.21741/9781644904152-36
The article was published as article 36 of the book Advanced Materials and Sustainable Energy Technologies
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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