A Summary of the Google Flood Forecasting Meets Machine Learning Workshop

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.