SaaS-Based Integrated Analysis System for Deep Stormwater Tunnels to Reduce Urban Flooding

Urban areas are increasingly facing the limitations of existing drainage systems due to the growing frequency and intensity of extreme rainfall caused by climate change. As underground space becomes more developed and urban infrastructure grows more complex, effective flood mitigation requires a planning tool capable of systematically evaluating large-scale drainage facilities such as deep stormwater tunnels. This study proposes a SaaS-based integrated analysis and design system that automates the entire process of rainfall–runoff simulation, tunnel operation optimization, and surface inundation assessment.

The system consists of three major components:
(1) a SWMM-based hydrologic and hydraulic simulation engine,
(2) an automated calculation module for determining the storage capacity, inlet configuration, pumping requirements, and diversion channel sizing of deep stormwater tunnels, and
(3) a cloud-based data environment for storing, managing, and visualizing large hydrologic and topographic datasets.

Input data—including terrain, catchment characteristics, sewer networks, and design storm information—are automatically processed in the cloud database. The computation server conducts repeated SWMM simulations for various rainfall scenarios, generating results related to pipe surcharge, water-level variation, and overall drainage performance. A 2D inundation model is integrated to assess surface flooding before and after the construction of a deep stormwater tunnel, enabling spatial comparison of flood reduction effects.

The system outputs include required tunnel storage volume, surcharge-prone locations, inundation depth maps, and comparative scenario analyses. Users can easily generate, modify, and store multiple design alternatives and share them within a project team. This integrated modeling environment significantly improves the efficiency of complex drainage analyses and enhances the reliability of decision-making for urban flood mitigation infrastructure.

Overall, the proposed system serves as a next-generation digital tool that supports intuitive and comprehensive evaluation of urban drainage conditions. It is expected to markedly improve the practical applicability and planning efficiency of deep stormwater tunnel projects in future urban flood management efforts.

 

Acknowledgements

This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Technology development project to optimize planning, operation, and maintenance of urban flood control facilities, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00398012)