Pi 5 Natural Disaster Early Warning Hub — an intermediate-level kit for teens.

Raspberry Pi 5 Multi-Hazard Early Warning Hub — Build AI-Driven Earthquake, Flood & Storm Alerts

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 1-5 days.

India faces monsoons, cyclones, and seismic tremors that can escalate with little notice. This kit empowers you to build a compact, connected sensor hub that fuses seismic vibration, water level, and wind speed data on a Raspberry Pi 5. Run a pre‑trained machine learning model directly on the edge to classify normal conditions versus early earthquake rumbles, rising floodwater, or storm‑force gusts — and trigger alerts before danger becomes a headline.

What You'll Build

You will assemble a wall‑mount sensor array, wire dual MPU6050 IMUs to detect ground acceleration, ultrasonic rangefinders to measure water depth in a model flood basin, and a cup‑type anemometer to capture wind velocity. The Pi 5 ingests all sensor streams, applies a multi‑class scikit‑learn classifier, and lights an LED strip or pushes a Telegram notification the moment a hazard threshold is crossed. The station logs everything to the bundled NVMe SSD for later analysis, making it a serious data‑logging platform you can iterate on.

What You'll Learn

• Program Python scripts to poll I2C and GPIO sensors simultaneously on Raspberry Pi 5 — managing two MPU6050s, two HC‑SR04s, and one anemometer without timing conflicts.
• Preprocess raw sensor signals: extract acceleration RMS, ultrasonic time‑of‑flight smoothing, and wind speed averaging to build a clean feature vector for ML.
• Train and optimise a lightweight Random Forest classifier on historical data, then deploy it on‑device with joblib for sub‑100‑ms inference.
• Design a real‑time monitoring dashboard using Streamlit, graphing live sensor feeds and logged events — a skill directly transferable to industry IoT roles.

Kit Contents