UrbanAI — Wellington AI

City Overview

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Population—

Urban Area—

Ward Count—

Base Rainfall—

Runoff Coefficient—

Hub Count—

Hub Capacity—

Storage Buffer—

Selected Scenario: —

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Floodwater Volume (m³)

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Post-Storage Volume (m³)

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Hub Clearance (hours)

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Distributed Pump Clearance (hrs)

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Effective Rainfall (cm) at selected scenario ·

Financial Snapshot —

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Total CAPEX ()

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Annual Net Benefit ()

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Simple Payback (years)

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30-Year IRR

System Design Reference

UrbanAI models a four-layer adaptive infrastructure system inspired by empirical analysis of Chennai's flood resilience requirements and validated against global city flood management literature. The platform evaluates ward-hub infrastructure against distributed pump deployment, demonstrating how centralised hub architecture reduces clearance time by routing water through interconnected storage assets — sequential lake drainage, circular underground flow, directional canal systems, and emergency sea discharge pathways — each activated adaptively based on rainfall intensity.

Chennai Reference Model
100 wards · 70 cm design event · 28 billion litres · 2,150 hub pumps replacing 215,000 distributed units

Mumbai Benchmark
Stable to 70 cm · Connected underwater system with sea outfall · Reference architecture for hub-and-canal design

Global Generalisation
Single computation engine parameterised across 14 cities · 4 continents · 5 currencies · 6 extreme rainfall scenarios

L1 — Rainfall Volume Computation

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Total Floodwater Volume (m³)

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Rainfall (cm at scenario)	—
Urban Area	—
Runoff Coefficient	—
Detention Storage	—
Post-Storage Volume	—
Storage Absorption %	—

L2 — Hub vs Distributed Clearance

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Hub Clearance (hours)

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Distributed Clearance (hours)

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Clearance Time Saving (hub vs distributed)

Composite Resilience Score

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Overall Score / 100

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Hub Clearance Speed (30%)

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Storage Efficiency (20%)

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Clearance Saving (25%)

Pump Type Optimisation — Per Hub Configuration

Pump Type	Capacity	Capacity Share	Units per Hub	Total Fleet (all hubs)	Share of Hub Capacity

Note: Pump mix achieves the target hub capacity through a blend of 40% Type D (high-power) + 30% Type C + 20% Type B + 10% Type A (base flow maintenance). Exact procurement mix subject to detailed engineering survey.