⬤ Prototype Stage
Embedded Systems · IoT · Urban Intelligence · Public Safety

Flooding,
understood.
Not just warned.

FloodSentinel is a two-in-one urban flood monitoring and intelligence system. It protects lives with real-time alerts — and gives cities the data they need to fix flooding at its source. Continuously valuable, not just in rainy season.

40m
Warning lead time
24/7
Year-round operation
~$85
Cost per sensor node
SMS
No internet required
Live Demo

See it working — South C, Nairobi

14:00:00
📍
South C — Ngong River Corridor
3 active nodes · Drainage channel monitored · GSM armed
Risk profile
HIGH
Water Level — Ngong River Nodes
↑ Monitoring...
SC-Node 1
Lang'ata Rd Bridge
SC-Node 2
South C Estate
SC-Node 3
Mombasa Rd end
Safe <0.5m
Watch 0.5–0.75m
Critical >0.75m
🌧
Simulation starting...
Watch water levels rise across South C.
01

The problem we're solving

🌊

Zero warning time

Communities across Nairobi receive no advance notice when conditions turn dangerous — whether from rivers overflowing or drainage channels backing up under heavy rain. Floods arrive in minutes.

0 min
🏘

High-density exposure

Over 200,000 Nairobi residents live in areas where both river flooding and overwhelmed drainage systems cause regular inundation. Displacement and property loss recur every rainy season.

200K+
🚧

Failing drainage infrastructure

Much of Nairobi's drainage network is aging, under-maintained, and unmapped in terms of real performance. No system currently measures how quickly drainage channels fill, back up, or fail — so problems only get discovered after a flood.

Unmeasured
02

A two-in-one system

⚡ Layer 1 — Immediate Safety

Real-time alerts that save lives

When water rises upstream, FloodSentinel detects it immediately and pushes warnings to communities before conditions become dangerous.

Detects rising water levels at upstream sensor nodes in real time
Sends SMS alerts 20–40 minutes before flood reaches communities
Three-tier system: Watch → Warning → Critical escalation
No internet required — GSM reaches the most vulnerable populations
Simultaneously notifies residents, ward administrators, and emergency services
📊 Layer 2 — City Intelligence

Continuous data that fixes the root cause

The same sensors that trigger alerts are also building a long-term picture of how flooding behaves across the city — every day, flood season or not.

Tracks flood frequency — how often events occur at each location
Measures flood intensity — how severe conditions become per event
Monitors rise rate — how quickly dangerous conditions develop
Maps location patterns — where water accumulates consistently
Reveals drainage system performance — identifies where infrastructure is failing
Safety loop
Detect Alert Protect
Intelligence loop
Store Analyze Improve

"FloodSentinel doesn't just warn people about floods. It helps the city understand where flooding happens, how severe it is, and where drainage systems are failing — so the right areas can be fixed."

03

System architecture

FloodSentinel uses a distributed network of low-power sensor nodes placed at strategic river points upstream. Each node operates independently and communicates via GSM — no Wi-Fi or mains power required.

01

ESP32 Microcontroller

Central processing unit per node. Handles sensor reads, threshold logic, and communication triggers.

HW
02

Ultrasonic Distance Sensor (HC-SR04)

Non-contact water level measurement. Mounted above river surface, accurate to ±3mm.

HW
03

SIM800L GSM Module

Sends SMS alerts to registered residents, ward administrators, and emergency services automatically.

COMM
04

Solar Panel + LiPo Battery

Fully off-grid power. Nodes operate continuously with no dependency on grid infrastructure.

HW
05

Threshold Alert Logic (Firmware)

Three-tier alert system: Watch → Warning → Critical. Rise rate detection prevents false alarms.

SW
06

Data Logging to Cloud Dashboard

Historical level data sent via MQTT to a lightweight web dashboard for county water authorities.

SW

How a flood event flows through the system

1

Sensor reads water level every 60 seconds

HC-SR04 fires ultrasonic pulse. ESP32 calculates distance-to-surface and converts to absolute river depth using baseline calibration.

2

Firmware checks against dynamic thresholds

Not just absolute level — rise rate (cm/hr) is calculated. A slow rise at 0.9m may be safe; a rapid rise at 0.6m triggers Watch status.

3

Alert level assigned: Watch / Warning / Critical

Each tier escalates the response. Watch notifies the ward office. Warning triggers resident SMS. Critical triggers all channels simultaneously.

4

SIM800L dispatches SMS alerts

Pre-registered numbers receive plain-language alerts in English and Swahili. No app, no internet, no smartphone required on the resident's end.

5

Data logged to cloud dashboard

All readings pushed to MQTT broker → lightweight dashboard. County authorities get historical trends, node health status, and event logs.

04

Why this isn't a seasonal project

The concern with flood systems is always: "what does it do when it's not raining?" FloodSentinel's answer is: it keeps working. Even on a dry day, the network is collecting baseline data, monitoring drainage channel behaviour, and refining its picture of how water moves across the city.

Continuous baseline monitoring of water channels, wet or dry
Drainage efficiency scores — does a channel clear in 1hr or 6hrs post-rain?
Historical dataset accumulates with every passing week
County authorities access trends year-round, not just during emergencies

What gets built over time

🗺
Flood risk map of Nairobi
Street-level heatmap of where flooding is worst and most frequent
🔧
Drainage failure index
Evidence-based ranking of which infrastructure needs urgent repair
📈
Year-on-year trend analysis
Is flooding getting worse or better in a given area over time?
🏛
Infrastructure budget justification
Data counties can use to prioritise and fund drainage improvements
05

Key design decisions

Why not use existing systems?

Last-mile gap that no one else fills

Kenya Met and Google Maps provide general forecasts and regional data. FloodSentinel provides hyper-local, real-time detection at street and drainage level — especially in informal settlements and poorly mapped urban areas that existing systems ignore entirely.

How is accuracy maintained?

Trend detection over single-point readings

Rain interference, debris, and mounting conditions affect any sensor. FloodSentinel handles this through per-node baseline calibration, emphasis on rise-rate rather than absolute level, and multi-threshold logic. The system tracks trends, not just snapshots.

Why GSM over internet?

Designed for the most vulnerable, not the most connected

GSM requires no smartphone, no Wi-Fi, no app. A plain SMS reaches anyone with any mobile phone. This is a deliberate accessibility choice — the communities most exposed to flooding are often the least connected.

How are false alarms prevented?

Three-factor alert logic

Alerts require absolute water level + rise-rate + multi-stage threshold confirmation. A slow rise at 0.9m stays at Watch. A rapid rise at 0.6m triggers Warning. This prevents normal fluctuations from generating unnecessary alerts and eroding community trust.

What about maintenance?

Low-cost, modular, community-serviceable

Nodes are designed for easy replacement of individual components. Each node reports its own battery level and GSM signal status, enabling proactive maintenance. At ~$85 per node, replacement is affordable at ward or community level without central oversight.

How does it scale?

Horizontally, without infrastructure dependency

Each node operates independently — no mesh networking, no central hub required. Adding a new location means installing one node. The system scales across an entire city with no increase in coordination complexity and no dependency on fixed internet infrastructure.

06

Projected impact at scale

38
Minutes of warning
before downstream impact
365
Days per year the system
is actively collecting data
~$85
Per node — deployable
at community scale
0
Internet required on
the resident's device
07

Build timeline — 10 weeks

Wk 1–2

Component procurement, sensor calibration, ESP32 environment setup

Wk 3–4

Firmware: level reading, threshold logic, rise-rate algorithm

Wk 5–7

GSM alert integration, SMS dispatch, field enclosure & solar wiring

Wk 8–10

Dashboard, field testing at Nairobi River, demo & documentation