Blog
The latest news from Google AI
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.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
