Urban Flood Nowcasting System (Drainage and Rainfall Coupling)
“Indian cities flood instantly during heavy rains because we cannot predict exactly which streets will turn into rivers. This software solution connects live weather radar data with digital maps of city streets and underground drainage networks to forecast street-level water logging 3 hours in advance. It helps citizens navigate safely and allows authorities to take preemptive action.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
A real-time, GIS-based software platform and coupled simulation pipeline that integrates rainfall nowcasts, Digital Elevation Models (DEM), and graph-based urban drainage networks to predict street-level flooding (0-3 hour lead time).
WHO IS IT FOR?
Primary beneficiaries and users include municipal corporations (BMC, MCD), disaster management authorities (NDMA), emergency rescue teams, public transit operators, and everyday commuters navigating through heavy downpours.
WHY DOES IT EXIST?
Traditional weather forecasting predicts city-level rainfall amounts but fails to account for micro-topography, concrete surfaces, and clogged or narrow underground drainage pipes. Municipal bodies lack real-time predictive tools, resulting in reactive disaster management, traffic gridlocks, and loss of life.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Doppler Weather Radar (DWR) rainfall raster data, high-resolution Digital Elevation Models (DEM), underground stormwater network specifications (manhole locations, pipe diameters, slopes as a directed graph), and live IoT/crowdsourced water level data.
Coupled hydraulic and hydrological modeling: routing surface runoff over 2D terrain grids while simultaneously simulating pipe flow via a graph-based mathematical network to detect node overcapacity, pressure backflow, and surface inundation depth calculation.
A dynamic web-based GIS dashboard showing street-by-street water depth estimations in centimeters across a 0-3 hour sliding window, automated alerts for hazardous zones, and a routing API for navigation apps to suggest safe detours.
CONSTRAINTS
- Must operate within a 0-3 hour ultra-short forecasting window (nowcasting).
- Requires high computational efficiency to run heavy hydraulic simulations (SWMM-like logic or ML surrogates) in near real-time.
- Must handle missing or incomplete underground drainage map data common in older Indian urban areas.
- Dashboard must remain responsive on low-bandwidth networks during emergency scenarios.
JURY METRICS
- Prediction accuracy of inundation depth (Mean Absolute Error in cm compared to ground truth sensors).
- Inference latency and end-to-end execution time from radar update to dashboard render (< 2 minutes).
- False alarm rate for critical street flood warnings.
- UI/UX clarity and ease of interpretation for non-technical disaster management operators.
CORE BOTTLENECK
Coupling high-resolution 2D surface terrain flow with 1D underground pipe hydraulic networks without blowing up computational complexity or exceeding real-time processing limits.
Why You Should Choose This PS (Pros)
- Extremely high national priority and social impact addressing recurring urban calamities in Indian metros.
- Clear, structured deliverables: Data pipeline, simulation backend, interactive GIS dashboard, and routing API.
- Great potential for a 'wow factor' by visualizing water spreading across 3D terrain and map interfaces.
Challenges & Risks to Watch (Cons)
- Complexity of underlying fluid dynamics and hydraulic modeling (St. Venant equations or simplified wave equations).
- Scarcity of ground-truth underground drainage network GIS shapefiles for public testing during a hackathon.
- Crowded domain if teams only build a generic weather app without the crucial drainage-rainfall coupling.
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
Urban flooding in major Indian metros like Mumbai, Delhi, and Chennai has become an annual crisis. Traditional Numerical Weather Prediction (NWP) models fall short because knowing how much rain will fall does not automatically translate into knowing where the streets will flood.Urban flooding is a hyper-local phenomenon dictated by micro-topography, concrete imperviousness, and heavily strained, invisible drainage networks. Currently, municipal bodies lack real-time, street-level predictive systems. Consequently, cities are caught off guard by rapid water accumulation, leading to severe traffic gridlocks, economic disruption, and loss of life.
The challenge is to design a high-resolution, real-time Urban Flood Nowcasting System (0–3 hour lead time) capable of predicting street-level inundation before it happens.
Participants must move away from isolated weather models and instead build a coupled framework. This system must fuse real-time rainfall nowcasts with high-resolution Digital Elevation Models (DEM) and a graph-based mathematical model of the city’s underground drainage network. By mapping how water flows, accumulates, and surcharges across concrete surfaces and drainage nodes, the solution should pinpoint exactly which streets or intersections will flood.Develop a pipeline that takes high-resolution rainfall nowcasts (from Doppler Weather Radars) and instantly routes that volume across a 2D surface terrain model. Represent the city's stormwater drain network as a directed graph (nodes as manholes/inlets, edges as pipes/canals). The model must calculate hydraulic capacity and predict where blockages or overcapacity will cause backflow onto the streets. A dynamic, web-based GIS dashboard showing real-time, street-by-street flooding projections (e.g., water depth estimations in centimeters) with a 0–3 hour forward-looking window.An API utility that can interface with navigation maps to suggest flood-safe alternative routes for emergency services, public transit, and commuters during heavy downpours.