A warning before the danger — how it actually works
Floods, fires, crime, waste and severe weather all move faster than the warnings people get today. AlertCommunities closes that gap: citizens report, agencies verify, sensors and AI predict — and alerts reach the right streets in minutes, on any phone.
Five services and a family safety net
Every service follows the same loop: report → verify → alert → resolve. Citizens and trusted feeds report; the responsible agency reviews inside its jurisdiction; verified incidents go live on the map and alert everyone nearby who subscribed.
💧 Flood
Sensor stations on drains and rivers, AI nowcasts, street-level warnings 30–120 minutes ahead — the deepest service, covered in detail below.
🔥 Fire
Structure and bush fires, gas leaks, smoke sightings — reviewed by the fire service, mapped live so neighbours evacuate early.
🛡️ Crime
Theft, assault and suspicious activity, visible to responders by default and published only when the police approve — trust without vigilantism.
♻️ Waste
Illegal dumping and choked gutters — the slow emergencies that cause the fast ones when the rains come.
⛅ Weather
Live conditions, 24-hour rain outlook, air quality and rain radar, fused into every flood-risk score.
🧡 SafeNet
Your family's safety net: consent-first live location for people and assets, danger-zone alerts, SOS — on the same live map.
Four layers, one job: time to move
Flood is our deepest service — a physical sensor network feeding the AI. Each layer works on its own, and the whole network gets smarter every rainy season.
1 · Sensing
Solar-powered nodes with radar and ultrasonic water-level sensors, a rain gauge, temperature/humidity and atmospheric-pressure sensors — GPS-located, tamper-detecting, in flood-rated IP67 enclosures. Each node reports every 60–120 seconds and runs 5+ days on battery backup alone.
2 · Connectivity
A LoRaWAN mesh (10+ km per gateway) is the low-power backbone; LTE-M/Cat-M1 cellular takes over automatically if it fails — so the network stays up at the exact moment it matters most.
3 · Intelligence
A cloud AI engine fuses sensor streams with GMet radar and satellite rainfall data, producing street-level flood-risk scores refreshed every five minutes — and retraining after every event.
4 · Action
Tiered alerts — advisory → warning → evacuate — go out over SMS, WhatsApp, USSD and the public app in English and local languages. No smartphone or data plan required.
From raindrop to warning in minutes
One path, end to end. Rain falls on the upper Odaw catchment; a sensor sees the river rise; the AI recognises the pattern; and phones across Kaneshie and Circle buzz — 30 to 120 minutes before the water arrives, with a clear path to 3–6 hours as the network matures.
What an AlertCommunities afternoon looks like
A real scenario, minute by minute, during a heavy rainfall event over the Odaw basin.
Heavy rainfall starts over Achimota. Upstream sensors log rising intensity.
Water at the Avenor bridge node passes the warning threshold, climbing 4 cm per minute.
The model projects the flood wave will reach Kwame Nkrumah Circle in ~55 minutes.
SMS and WhatsApp warnings reach residents, traders and drivers across the corridor. NADMO's dashboard flashes the risk heat-map.
Response teams pre-position at Circle and Odawna. Market traders move goods to higher ground.
Police divert traffic away from the underpass before it becomes a trap.
Water covers the road at Circle — but this time, the people and goods are already gone.
Doesn't Accra already have flood warnings?
Yes — regional forecasts and radio announcements. They matter, but they were never designed to tell a trader at Kaneshie that her street floods in 50 minutes. That's the layer we add.
| Today's systems | AlertCommunities |
|---|---|
| City-wide weather forecasts | Street-level sensors on the actual drains and rivers |
| Regional warnings, hours-old data | Hyperlocal alerts refreshed every 5 minutes |
| Manual observation and phone calls | Automated real-time monitoring, 24/7 |
| Reactive — respond after flooding starts | Predictive — 30–120 min ahead, targeting 3–6 hrs |
| Limited public visibility | Open live map anyone can check on any browser |
| Commercial flood systems built for rich cities | Community-owned, ~$3,200 all-in per station |
Learning with the network
Every station is also a classroom. Our education programme turns the sensor network into hands-on STEM learning for Accra's schools.
School visits & open data
Students explore live water-level data from their own neighbourhood and learn how rainfall becomes a flood — with free classroom datasets.
Build-a-node workshops
Hands-on electronics workshops where students assemble and test a real sensor node with our engineers and Ghana Planetarium educators.
Flood-warden training
Community volunteers learn to validate sensor readings, report ground truth, and lead their street's response when a warning lands. Join the programme →
Common questions
Do I need a smartphone to get alerts?
No. Alerts arrive by plain SMS and USSD as well as WhatsApp — no smartphone, no app install, no data plan required.
How far ahead can AlertCommunities warn me?
Today the pilot delivers 30–120 minutes of lead time depending on where rain falls in the catchment. As sensor density and the AI model grow, we're targeting 3–6 hours.
What stops the sensors being stolen?
Every node has built-in tamper detection and GPS location, uses low-resale-value enclosures, and is adopted by community flood wardens who own its upkeep. Rapid low-cost replacement is built into the budget.
What happens when the mobile network goes down in a storm?
The primary link is LoRaWAN radio — independent of the mobile network — with LTE-M/Cat-M1 cellular as automatic failover. Nodes also cache readings and retransmit when reconnected.
Who pays for this long-term?
Grants fund the build-out; operations are sustained by government platform licences, insurance data feeds and corporate risk subscriptions — so the network outlives the grant cycle. More on the model →
Safety guides, written for here
Country-aware guides for every service — and a tutor that answers from them.
Ask the safety tutor
Flood preparedness (Ghana)
Odaw-basin specifics: NADMO on 112, drain-clearing before the majors, go-bag basics.
fireFire safety at home and market
Prevention around LPG and wiring, the first 60 seconds, and safe evacuation.
crimeCommunity crime awareness
Practical prevention, safe reporting, and looking out for each other.
wasteWaste & drainage: the flood connection
Choked gutters cause floods. What to do about dumping, burning and blocked drains.
safetyProtecting your family with SafeNet
Consent-first location sharing, safe zones, SOS — how to set it up well.
Now that you know how it works — help it grow
Every station protects thousands of people. Every donation buys minutes of warning.