NATURAL-DISASTER-PREDICTION-SYSTEM

LANDSLIDE ⛰📈 PREDICTOR

A comprehensive AI-Powered embedded system designed to predict the possibility of landslides in an area based on real-time environmental sensor data and machine learning analysis.

Features

• Real-time Monitoring: Collects environmental data using Arduino-based sensors.
• Data Processing: Processes sensor data for soil moisture, temperature, humidity and light intensity.
• Local Storage: Stores sensor readings in local database for offline analysis.
• Remote Storage: Syncs data with remote database for backup and distributed access.
• Machine Learning: Uses scikit-learn models to predict landslide probability.
• Visualization: Interactive plots and graphs using matplotlib and seaborn.
• Hardware Design: Complete electronic system design in Proteus.
• Web Interface: Online dashboard for monitoring and predictions.

Project Structure

NATURAL-DISASTER-PREDICTION-SYSTEM/

├── APPLICATION/  📊

│   └── NATURAL DISASTER PREDICTION SYSTEM.ipynb  🤖 ML model training notebook

├── DATABASE/     💾

│   ├── msodbcsql_2.msi                           🔌 SQL driver

│   ├── msodbcsql.msi                             🔌 SQL driver

│   ├── Plant & Environmental Data.sql            📝 SQL database

│   └── Plant & Environmental Data.sql.bak        💾 Backup file

├── DOCUMENTATION/ 📚

├── HARDWARE/      🔧

│   ├── environmentalData_MainClass_PrimaryArduino/    

│   │   └── environmentalData_MainClass_PrimaryArduino.ino     🎯 Primary Arduino code

│   ├── environmentalData_MainClass_SupportArduino/

│   │   └── environmentalData_MainClass_SupportArduino.ino     🔄 Support Arduino code

│   └── environmentalData_MonitorClass_SupportArduino/

│       └── environmentalData_MonitorClass_SupportArduino.ino  📡 Monitoring code

├── INTERFACE/     🖥️

├── PROTEUS/       ⚡

│   ├── Electronic Design Files

│   └── Circuit Simulations

└── SCHEMATICS/    📐

    └── SOFTWARE DESIGN/

        ├── arduino class diagram.uxf           📊 Class diagram

        ├── arduino E-R diagram.uxf             📊 E-R diagram

        ├── arduino sequence diagram.uxf        📊 Sequence diagram

        └── arduino use case diagram.uxf        📊 Use case diagram

Hardware Components

• Arduino microcontroller
• Soil moisture sensors
• Temperature and humidity sensors
• Light intensity sensors
• LCD display
• Buzzers for alerts
• Relay for fan control
• Stepper motor controller

Getting Started

Prerequisites

• Arduino IDE
• Python 3.8+
• Proteus 8.7+
• Required sensors and components
• Web browser

Hardware Setup

1. Follow the Proteus circuit design to assemble the hardware components
2. Upload the Arduino code to the microcontroller:

arduino-cli compile --upload Arduino_Code.ino

Software Installation

1. Clone this repository:

git clone https://github.com/N-Elmer/NATURAL-DISASTER-PREDICTION-SYSTEM.git
cd NATURAL-DISASTER-PREDICTION-SYSTEM

2. Install Python dependencies:

pip install -r requirements.txt

3. Access the web interface at:

https://s7ac6zkycfusqzuh.anvil.app/D3COVOGNRLN7VLXFJ3FJ7DD2

Key Features

Data Collection

• Real-time sensor readings for:
  • Soil moisture levels
  • Temperature
  • Air humidity
  • Light intensity

Data Processing

• Signal conditioning
• Noise filtering
• Data normalization
• Feature extraction

Prediction System

• Machine learning models for landslide prediction
• Real-time probability assessment
• Historical data analysis
• Alert generation

Visualization

• Interactive dashboards
• Time-series plots
• Sensor data graphs
• Prediction confidence metrics

Dependencies

• Hardware:

  • Arduino libraries
  • Sensor drivers
  • LCD library

• Software:

  • pandas: Data manipulation
  • scikit-learn: Machine learning
  • matplotlib: Data visualization
  • seaborn: Statistical plotting

Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

License

This project is licensed under the Apache License - see the LICENSE file for details.

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Web Interface: Live Demo

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