Real-Time Disaster Information Aggregation Software

A comprehensive platform to aggregate, analyze, and display disaster-related data from multiple sources, facilitating efficient and timely disaster response. This project was built for SIH Internals at KIIT, 2024

Problem Statement

Smart India Hackathon 2024 - Problem Statement ID: 1687
Title: Real-Time Disaster Information Aggregation Software
Theme: Disaster Management

Overview

This project addresses the need for a unified platform that provides real-time disaster updates from diverse data sources, ensuring swift response and enhanced coordination among rescue teams, affected individuals, and administrators.

Key Features

• Data Aggregation: Collects information from social media, news sites, government sources, and satellite imagery using web scraping and predictive ML models.
• User Interfaces: Tailored interfaces for three user types:
  • Normal Users: Report disasters and send SOS signals.
  • Rescue Teams: Locate and assist affected individuals.
  • Admins: Monitor and coordinate disaster response efforts.
• Offline Capabilities: Enables SOS signaling and user communication without internet through Bluetooth Low Energy (BLE) and WiFi Direct.
• Three-Layer Response System: Structured response based on disaster severity to optimize resource allocation.

Unique Value Propositions

• Multi-Source Data Integration: Combines diverse data streams (social media, news, satellite) for a comprehensive view.
• Predictive Analytics: Uses ML models to forecast potential disasters for early preparation.
• Offline Communication: Allows essential communication during network outages.
• Customizable Response: Tailors response actions based on disaster scenarios.

Technical Approach

• Frontend: React (web), Java (app)
• Backend: Python, Flask
• APIs: RESTful APIs
• Authentication: JWT/OAuth
• Databases: MongoDB (unstructured data), PostgreSQL (structured data)
• Data Processing & ML: TensorFlow, scikit-learn
• Web Scraping: BeautifulSoup, Scrapy
• Real-Time Communication: WebSockets, Socket.io; GPS, Mesh Networks for offline
• Data Visualization: D3.js, Chart.js

Feasibility Analysis

• Technical: Leverages proven tech stacks and libraries for efficient development.
• Financial: Potential for grants and partnerships with NGOs and government agencies.
• Market: High demand for disaster response solutions among government agencies, NGOs, and relief organizations.
• Operational: Cross-platform support and real-time data processing ensure timely disaster updates.

Challenges and Mitigation Strategies

• Technical: Modular design for seamless integration and load balancing for scalability.
• Financial: Scalable cloud services to optimize costs and diverse funding sources.
• Market: Pilot programs to showcase value and highlight unique features.
• Operational: Encryption for privacy and offline functionality with GPS and mesh networks.

Impact and Benefits

Positive Impacts

• Improved Disaster Response: Real-time data ensures faster response times.
• Cost Savings: Optimizes resources to reduce financial losses.
• Community Empowerment: Provides tools for communities to report and receive aid.

Potential Benefits

• Social: Enhances access to real-time disaster information.
• Economic: Reduces downtime and creates new market opportunities.
• Environmental: Increases energy efficiency and reduces waste.

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References

• Pichiyana, V., et al. "Web Scraping using Natural Language Processing," Procedia Computer Science, 2023.
• Kaur, P., "Sentiment analysis using web scraping for live news data with ML," Materials Today: Proceedings, 2022.
• Ishiwatari, M., "Leveraging Drones for Effective Disaster Management," Progress in Disaster Science, 2024.