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A Summary of the Google Flood Forecasting Meets Machine Learning Workshop
Monday, March 18, 2019
Posted by Sella Nevo, Senior Software Engineer and Rainier Aliment, Program Manager
Recently, we hosted the
Google Flood Forecasting Meets Machine Learning
workshop in our Tel Aviv office, which brought hydrology and machine learning experts from Google and the broader research community to discuss existing efforts in this space, build a common vocabulary between these groups, and catalyze promising collaborations. In line with
our belief
that machine learning has the potential to significantly improve flood forecasting efforts and help the hundreds of millions of people affected by floods every year, this workshop discussed improving flood forecasting by aggregating and sharing large data sets, automating calibration and modeling processes, and applying modern statistical and machine learning tools to the problem.
Panel on challenges and opportunities in flood forecasting, featuring (from left to right):
Prof. Paolo Burlando
(ETH Zürich),
Dr. Tyler Erickson
(Google Earth Engine),
Dr. Peter Salamon
(Joint Research Centre) and
Prof. Dawei Han
(University of Bristol).
The event was kicked off by Google's
Yossi Matias
, who discussed
recent
machine
learning
work and its potential relevance for
flood forecasting
,
crisis response
and
AI for Social Good
, followed by two introductory sessions aimed at bridging some of the knowledge gap between the two fields - introduction to hydrology for computer scientists by
Prof. Peter Molnar
of ETH Zürich, and introduction to machine learning for hydrologists by
Prof. Yishay Mansour
of Tel Aviv University and
Google
Included in the 2-day event was a wide range of
fascinating talks
and
posters
across the flood forecasting landscape, from both hydrologic and machine learning points of view.
An overview of research areas in flood forecasting addressed in the workshop.
Presentations from the research community included:
Dr. Dhanya C. T.
of IIT Delhi gave a talk on satellite precipitation error characterization.
Adarsh M. S.
, Assistant Director of the Indian Ministry of Water Resources presented India's
Central Water Commission's
role and challenges.
Prof. Andras Bardossy
of the University of Stuttgart discussed variation in discharge series and the challenges this presents.
Frederik Kratzert
of Johannes Kepler University presented recent work on hydrologic modeling using
LSTMs
.
Prof. Paul Bates
of the University of Bristol gave a keynote on the potential uses of machine learning in inundation modelling.
Prof. Emmanouil Anagnostou
of the University of Connecticut spoke about hyper-resolution hydrologic simulations at global-scale.
Prof. Efrat Morin
of the Hebrew University highlighted flood prediction challenges in dry climate regions.
Dr. Zachary Flamig
of the University of Chicago presented NASA's new
global flash flood prediction project
.
Alongside these talks, we presented the various efforts across Google to try and improve flood forecasting and foster collaborations in the field, including:
Vova Anisimov presented our progress in hydraulic modeling.
Ami Weisel presented our research on
remote discharge estimation
.
Stephan Hoyer presented our work on
data-driven discretization approach to solving partial differential equations
.
Jason Hickey presented our efforts using machine learning for precipitation prediction.
Avinatan Hassidim presented lessons learned from
previous
projects
in
Google
, and how they apply to our
flood forecasting efforts
.
Additionally, at this workshop we piloted an experimental "ML Consultation" panel, where Googlers Gal Elidan, Sasha Goldshtein and Doron Kukliansky gave advice on how to best use machine learning in several hydrology-related tasks. Finally, we concluded the workshop with a moderated panel on the greatest challenges and opportunities in flood forecasting, with hydrology experts
Prof. Paolo Burlando
of ETH Zürich,
Prof. Dawei Han
of the University of Bristol,
Dr. Peter Salamon
of the Joint Research Centre and
Dr. Tyler Erickson
of Google Earth Engine.
Flood forecasting is an incredibly important and challenging task that is one part of our larger
AI for Social Good
efforts. We believe that effective global-scale solutions can be achieved by combining modern techniques with the domain expertise already existing in the field. The workshop was a great first step towards creating much-needed understanding, communication and collaboration between the flood forecasting community and the machine learning community, and we look forward to
our continued engagement
with the broad research community to tackle this challenge.
Acknowledgements
We would like to thank Avinatan Hassidim, Carla Bromberg, Doron Kukliansky, Efrat Morin, Gal Elidan, Guy Shalev, Jennifer Ye, Nadav Rabani and Sasha Goldshtein for their contributions to making this workshop happen.
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