Introduction - Disasters are on rise, and due to change in climate-
there is a change in weather patterns. Disaster Management aims to reduce the
negative impacts of natural calamities and safeguarding the interest of people.
The pattern of Tsunami, catastrophic events can no longer be ignored. In
2018- In various part of the globe, the natural calamities have shaken the
world.
15 disaster events caused more
than $22 billion in damages in the United States in 2017, according to the
National Oceanic and Atmospheric Administration (NOAA). And that doesn’t
include the Northern California Wildfires or the biggest hurricanes.
Hurricane Harvey’s price tag
topped $125 billion, according to NOAA. Hurricanes Irma
($64-92 billion) and Maria ($40-80 billion) reduced much of Puerto Rico, the
U.S. Virgin Islands, and the Caribbean to ruins. And of course, these numbers
only tell part of the story. The humanitarian impact is incalculable.
The data has different number for
different countries. India, Bangladesh, Chile and Indonesia are the some of the
names which also lost in billions due to disasters.
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| Image Source - https://www.bbntimes.com/en/technology/machine-learning-can-save-us-from-disasters |
Challenges-
Effective monitoring of disasters is a global challenge, and detailed
information about disasters is unavailable and the available information
remains un-managed.
That
big information often does not help in getting the conclusions
Impacts of technologies like Big Data and Machine Learning-
Big data is a big and complex data sets that can not be executed by simple
application. Data sets grow daily and Big data methods can help in maintaining
and predicting the data.
Same way artificial intelligence or machine learning is
intelligence demonstrated by machines.
New technologies like Big Data and Artificial Intelligence can
execute information with more accuracy than human mind. It can store big amount
of data as well as can analyze it.
Security issues must be considered as there may be chances of data
tempering from some online enemies of the nation.
some of the most useful
data generated during a crisis comes from social media users and on-the-ground
aid workers. Images and comments from Twitter, Facebook, Instagram, and
Youtube, for example, can help experts make initial damage assessments. This
information can also help rescue workers find disaster victims more quickly,
while identifying and mapping new disaster sites in need of aid.
Finally, combining data
from satellite imagery, seismometers, with location-tagged social media
comments can help relief organizations to provide early warnings and verify
reports in real-time.
Identifying the population hotspot will be a lot easier with the
help of technologies.
Recent advances in machine learning and
artificial intelligence are allowing researchers, engineers, and scientists to
access and analyze new and bigger data sources than ever before.
Governments and relief
agencies are beginning to use these tools to coordinate better disaster relief
programs. For example, large-scale behavior and movement data, run through
predictive machine learning models, can help officials distribute supplies to
where people are going, rather than where they were.
Predictive analytics
programs like these are still in their early stages, but offer a promising new
approach to disaster relief.
In short, the role of AI
in disaster relief is to help governments and relief agencies parse through
large volumes of complex, fragmented data to generate useful information that
they can act on more quickly than before.
