use of AI to predict disasters to be specific, floods.
# hackathon_February_2026
use of AI to predict disasters to be specific, floods.
RiftGuard
A Hybrid AI + Physics Flood Intelligence and Emergency Coordination Platform for Kenya’s Rift Valley
1. The Challenge
How can AI help predict flooding, provide real-time alerts, and improve emergency coordination in high-risk regions?
Kenya’s Rift Valley faces complex rainfall dynamics driven by:
Convective storm systems (localized, intense rainfall)
Rapid elevation changes and steep basin slopes
Sparse weather station coverage
Limited real-time hydrological data
Fast runoff response times in river basins
Traditional global weather models perform poorly in such environments because:
Convective storms are small-scale and evolve rapidly
Coarse-resolution models average out localized extremes
Data scarcity limits calibration
Pure black-box AI models lack physical grounding
Flooding is not random. It is the result of measurable interacting variables. The problem is not disorder — it is insufficient interpretation.
2. Core Philosophy
Flooding is a physical process:
Rainfall → Infiltration → Surface Runoff → River Discharge → Flood Risk
Our system does not rely on AI alone.
We combine:
Physics-based hydrological modeling (structure and interpretability)
Machine learning correction layers (pattern refinement)
IoT-driven local sensing (data density)
Lightweight edge interpretation (resilience and speed)
We are not replacing physics.
We are constraining machine learning with physics.
3. System Overview
RiftGuard consists of four integrated layers:
Layer 1 — Distributed IoT Micro-Climate Network
Low-cost sensor stations deployed across Rift Valley basins.
Each station measures:
Rainfall intensity (tipping bucket gauge)
Temperature
Humidity
Barometric pressure
Soil moisture (in agricultural zones)
River level (ultrasonic sensor at key points)
Wind speed/direction (optional)
Data transmission via:
GSM
LoRa
WiFi (where available)
Purpose:
Increase sp …