Skip to content

Local Chapter project

Supplement Disaster Detection using Social Media

NLPTwitter ScrapingPattern Recognition
Start date
October 24, 2022
Finish date
November 28, 2022
Project status
completed
Supplement Disaster Detection using Social Media

Challenge background

Australia has gone through several Disaster events in recent years , The floods in July 2022 Australia are some of the worst the country has ever experienced and have caused widespread devastation.. These are the same communities where we saw massive bushfires in 2019 and 2020 which resulted in the loss of shrubs and trees setting the scene for extreme flooding. Tens of thousands of Australians have had to evacuate their homes after devastating floods struck the eastern part of the country, resulting in several millions of dollars in damage

Experts say climate change is fuelling an increase in extreme weather across Australia, threatening to make bushfires, floods and droughts more common.

A report published last month by the United Nations Environment Programme (UNEP) and GRID Arendal predicts that wildfires will become more frequent and intense, with a global increase of extreme fires by 50 per cent by the end of the century. The increase in wildfires renders land barren, which leads to increased run-off and, therefore, floods and, later, drought.

AI and Machine Learning can play crucial roles in the forecast and monitor and manage these disaster events. While several technologies and mechanisms is already in place , one area that can significantly supplement and improve the efficiency of these managing these disaster events is leveraging Social media

Project timeline

  1. 1

    Week 1

    Collection of Dataset (Tweets, Images, Public Data sets related to Disaster events)

  2. 2

    Week 2

    Collection of Dataset (Tweets, Images, Public Data sets related to Disaster events)

  3. 3

    Week 3

    Data Pre processing

  4. 4

    Week 4

    Data Preprocesisng

  5. 5

    Week 5

    Model Building

  6. 6

    Week 6

    Model Validation & Fine tuning

  7. 7

    Week 7

    Reporting and final Packaging

  8. 8

    Week 8

    Wrapup

What you'll learn

NLP Processing and Segmentation, Twitter Text Extraction, Classification Models, Reporting

Get involved

What to expect from a Local Chapter project

First project

  • Welcomes beginners and experienced practitioners.
  • Focuses on education and collaborative delivery.
  • Produces open-source project work.

Benefits

  • Address a significant real-world problem with your skills.
  • Build your project portfolio.
  • Demonstrate your work to organizations and project partners.

Requirements

  • Working English communication.
  • A learning mindset.
  • Commitment to collaborative project work.