London: Scientists are combining Twitter, citizen science and
cutting-edge artificial intelligence (AI) techniques to develop an
early-warning system for flood-prone communities.
Researchers from the
University of Dundee in the UK have shown how AI can be used to extract data
from Twitter and crowdsourced information from mobile phone apps to build up
hyper-resolution monitoring of urban flooding.
Urban flooding is
difficult to monitor due to complexities in data collection and processing.
This prevents detailed risk analysis, flooding control, and the validation of
numerical models.
Researchers set about
trying to solve this problem by exploring how the latest AI technology can be used
to mine social media and apps for the data that users provide.
They found that social
media and crowdsourcing can be used to complement datasets based on traditional
remote sensing and witness reports.
Applying these methods in
case studies, they found these methods to be genuinely informative and that AI
can play a key role in future flood warning and monitoring systems.
“Sea levels have been
rising at an average rate of 3.4mm a year over the past decade. The extremes of
today will become the average of the future so coastal cities and countries
must take action to protect their land,” Dr Roger Wang said.
“We were particularly
interested in the increased incidence of what we call sunny day flooding that
occurs in the absence of any extreme weather event due to the mean sea level
being higher,” he said.
“A tweet can be very
informative in terms of flooding data. Key words were our first filter, then we
used natural language processing to find out more about severity, location and
other information,” Wang said.
“Computer vision
techniques were applied to the data collected from MyCoast, a crowdsourcing
app, to automatically identify scenes of flooding from the images that users
post,” he added.
“We found these big
data-based flood monitoring approaches can definitely complement the existing
means of data collection and demonstrate great promise for improving monitoring
and warnings in future,” he said.
Twitter data was streamed
over a one-month period in 2015, with the filtering keywords of ‘flood’,
‘inundation’, ‘dam’, ‘dike’, and ‘levee’. More than 7,500 tweets were analysed
over this time.
MyCoast is a system used
by a number of environmental agencies to collect ‘citizen science’ data about
various coastal hazards or incidents.
The system contains over
6,000 flood photographs, all of which were collected through the mobile app.
The information extracted
by AI tools was validated against precipitation data and road closure reports
to examine the quality of the data.
Flood-related tweets were
shown to correlate to precipitation levels, while the crowdsourced data matched
strongly with the road closure reports.
The researchers believe a
tool like Twitter is more useful for large-scale, inexpensive monitoring, while
the crowdsourced data provides rich and customised information at the micro
level.
Source: DNA-27th December,2017
