🌍 IoT-Based Earthquake Detection Station

An intelligent real-time seismic monitoring system designed to detect and classify earthquake activity. The project is composed of two main components:

• 📡 Station Side – Collects seismic acceleration data and sends it wirelessly via UDP.
• 🖥️ Server Side – Receives, processes, and classifies data using machine learning, and visualizes the results via a web dashboard.

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🧱 System Architecture

A high-level overview of how data flows between the station and the server.

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🖥️ Server Side Overview

The server processes incoming seismic data and makes real-time predictions using a trained Random Forest model. It then updates a live web dashboard to notify users of potential earthquake activity.

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🔩 3D-Printed Enclosure Design

The station hardware is housed in a custom-designed 3D-printed case to protect and organize components.

Design Phase	Images
📦 Before Assembly
🧱 Case Cover
⚙️ Case Base
🔌 Pin Alignment
✅ Final Assembly
🌍 Website

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🛠️ PCB and Wiring Design

PCB and wiring layout for connecting the STM32 microcontroller to the accelerometer and Wi-Fi module.

Design Phase	Images
🖨️ PCB Design
🔌 Wiring Diagram

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🚀 Key Features

• Real-Time Seismic Detection – Uses a calibrated accelerometer to monitor ground motion.
• False Alarm Filtering with AI – A Random Forest model classifies real earthquakes from non-seismic events.
• Live Web Dashboard – Displays live events and predictions.
• Energy-Efficient Design – Activates only during vibration for optimal power usage.
• Modular and Scalable – Easily deploy multiple stations for broader coverage.

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🧠 How It Works

• The station samples acceleration data using an MPU-6050 sensor.
• If shaking is detected, the system transmits the first 200 acceleration readings via UDP.
• The server receives this data and classifies it using a pre-trained Random Forest model.
• If it’s an earthquake, the event is logged and displayed on a web interface.
• If its not an earthquake, the server chooses to ignore the data.

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📁 Project Structure

.
├── Design Autocad/             # 3D-printed enclosure and mechanical design files
├── Machine learning Tests/     # Model evaluation, training notebooks, and experiments
├── backend/                    # Flask API for data ingestion and prediction
├── database_init/              # SQL database schema and initialization scripts
├── frontend/                   # Web dashboard for visualizing seismic events
├── images/                     # System diagrams, 3D prints, and screenshots
├── stationSide/                # STM32 code, sensor logic, and UDP transmission
├── Grad_Thesis_Book_Latex.pdf  # The graduation book 
└── README.md                   # Project overview

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💡 Authors

• Abdulrahman Sallam
• Mohamed Abdelfattah

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