Event-driven flood system meets 15-minute updates for Hanoi city roads
An Event-Driven System Architecture for a 15-Minute Urban Flood Digital Twin: A Hanoi Case Study
Distributed, Parallel, and Cluster Computing
Summary
Floods can quickly cover roads in Hanoi during heavy rain, but current flood alerts only report after flooding happens. The authors created a computer system that uses weather data and simulations to predict flooding depths every 15 minutes. This system runs efficiently on common cloud and server setups, making fast flood maps and road flooding information available much sooner. They tested it using past storm data for Hanoi, showing the approach is possible though they did not check how accurate the flooding predictions were.
What this means in practice
- •For urban flood management teams: Provide automated, near-real-time flood depth data for city areas on commodity servers using combined cloud and local computing resources.
- •For emergency transportation planners: Access rapid updates on road flooding risks to inform routing policies without needing direct integration with flood simulation internals.
Tested on one dataset.
Authors
Mai Anh Tran, Vy Nguyen, Vova Nguyen
Abstract
Flash floods in Hanoi submerge roads within minutes of heavy rain onset, yet public flood advisories remain retrospective. Hydrodynamic models could supply forward-looking depth guidance, but they are typically run offline for historical events, and prior studies have not evaluated whether physics-based simulation, spatial serving, and vehicle-aware routing can operate together within a fixed refresh cycle using a cloud solver and a single commodity server. This paper evaluates an end-to-end prototype of an automated flood digital twin designed to meet a 15-minute refresh cycle under data scarcity. The architecture organizes execution into decoupled, event-driven workflows that chain weather-gated 3Di simulations through hydraulic restart states, serve flood depths through a dual-track spatial layer, and publish road-edge depths via standardized output contracts. Evaluated on a 57-run storm replay in the My Dinh and Tay Mo sector of Hanoi driven by satellite rainfall estimates, every cycle completed within the 15-minute deadline, publishing a ready edge table at a median of 537 s after slot start. Indexed raster tiles resolved point-depth lookups in a median of 0.54 ms (277 times faster than un-tiled rasters), while pre-rendered vector tiles served web maps without database queries. These findings demonstrate that a commodity server coupled to a cloud solver can sustain the 15-minute operational cycle and decouple routing policies from simulation internals. Because simulated depths were not validated against field gauges and the replay used observed satellite rainfall, the results establish system feasibility rather than forecast accuracy or route safety.