Exploration of Waterlogging Early Warning Technology for Municipal Drainage Systems under Extreme Rainfall

Abstract

This paper takes the prevention and control of urban flooding due to extreme rainfall in Shenzhen as the research object, focuses
on the demands of municipal drainage projects and urban flooding control projects, and constructs a full-chain solution of “early warning
technology - project implementation - resilience enhancement”. Through the collaborative innovation of multi-source heterogeneous perception networks (liDAR + fiber optic sensors) and physical-AI hybrid models (SWMM-STGCN architecture), a technological breakthrough
has been achieved with a flood prediction period of ≥30 minutes and a prediction accuracy NSE of >0.89, and it has also promoted the
transformation of drainage engineering systems towards intelligence and resilience. Empirical research shows that this system has shortened
the emergency response time during the 2024 flood season in Luohu District by 63.4% and reduced direct economic losses by 87.4%. From
the perspective of supervision, a smart water management assessment matrix with 137 indicators was pioneered. Based on early warning
information, a three-level resilience improvement path of “real-time regulation - facility coordination - planning and decision-making” was
constructed. Eventually, a policy-technology-governance integration model supporting Shenzhen’s smart water management goals during the
14th Five-Year Plan was formed. It provides a reference template that combines academic innovation with practical engineering value for
drainage engineering and urban flood control engineering.