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tharaka-university-hackathon-group-B/hackathon_February_2026

Domaine:

climate

Type de record:

project
Créateur:
tha
Hôte:
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 …

Visit

github.com

Languages

Kitharaka

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