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Hackenza_Website

Made by team Digital Dynamos:

  • 2022A7PS0497G (Aditya Bagla)
  • 2022A7PS0025G (Tarun Chauhan)
  • 2022B4A70073G (Vedant Kamath)
  • 2022B1A71033G (Mayukh Saha)

Project Description:

Softwares and Techstack used:

  • Leaflet (for map)
  • Javascript
  • Python
  • CSS
  • HTML

Objective:

The primary objective of this project is to provide a comprehensive solution for visualizing flood levels on streets and assisting users in finding the flood-safe zones during flood events. By processing rainfall data and integrating it with mapping technology, users can access real-time information about flood-prone areas and make informed decisions to ensure their safety and well-being.

Features

  • Flood Level Mapping: graphically depicts the flood intensity of every street
  • Flood safe zone finder: shows flood safe zones
  • Flood danger zone finder: shows flood danger zones
  • User friendly interface: easy to use

Target Audience:

  • General Public: Residents, commuters, and travelers can use the application to stay informed about flood risks and plan safe travel routes during inclement weather conditions.
  • Emergency Responders: First responders and emergency management agencies can leverage the tool to assess flood situations, coordinate rescue operations, and allocate resources effectively.
  • Government Agencies: Municipalities, local governments, and disaster management authorities can utilize the platform for flood risk assessment, mitigation planning, and public awareness campaigns.

Future Enhancements:

  • Real-Time Data Integration: Incorporate real-time rainfall and weather data for dynamic updates and more accurate flood level predictions.
  • Mobile Application: Develop a mobile application version for on-the-go access to flood information and safe zone finder functionality.
  • Community Engagement: Implement community engagement features such as crowdsourced reporting, user-generated content, and community forums to foster collaboration and information sharing among residents.

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  • HTML 61.2%
  • JavaScript 38.8%