Floods are the most common type of natural disaster, affecting more than 250 million people globally each year and causing around $10B in economic damages. As part of our efforts to advance AI to address the climate crisis and help communities affected, Google Research has developed AI models to forecast floods. Our Flood Forecasting Initiative aims to predict when and where riverine floods will occur, and alert people in areas that are about to be impacted before disaster strikes. By warning organizations and people in advance, we hope to empower them to act, limiting damage and loss of life. We work closely with governments, the UN, and NGOs to implement and distribute our flood alerts. After many years of intense research and development, our technology is now scalable and covers dozens of countries, and in the future we aspire to cover all areas affected by floods globally.
The Flood Hub provides users with locally relevant flood data and flood forecasts up to 7 days in advance so they can take timely action. It is a visual, easy-to-use resource that displays local riverine flood maps and water trends and gives real-time flood forecasts and alerts based on Google's AI models and global data sources. The Flood Hub is designed to meet the needs of governments, local aid organizations, and people directly at risk. All information is free of charge, publicly available, and can be shared over social networks. Forecasts are updated daily.
Flood Hub currently covers river basins across 80 countries worldwide, providing critical flood forecasting for over 1800 sites and covering a population of 460M people.
In 2023 we'll be publishing our forecasts via alerts on Google Search, Maps, and Android notifications to help more people access flood information.
The Hydrologic Model identifies whether a river is expected to flood by processing inputs like precipitation and other weather and basin data, and output a forecast for the water level in the river in the following days.
The Inundation Model simulates the behavior of the water as it moves across the floodplain based on the hydrology forecast and satellite imagery. This allows us to know which areas are going to be affected and how high we expect the water level to be.
By combining these models we are able to achieve unprecedented accuracy. This means we can provide forecasts that are more accurate and more actionable to empower governments, relief organizations, and citizens to take relevant actions and save lives.
Caravan-A global community dataset for large-sample hydrology, Frederik Kratzert, Grey Nearing, Nans Addor, Tyler Erickson, Martin Gauch, Oren Gilon, Lukas Gudmundsson, Avinatan Hassidim, Daniel Klotz, Sella Nevo, Guy Shalev, Yossi Matias. Nature Scientific Data, 2022.
Technical Note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks, Grey Nearing, Daniel Klotz, Jonathan Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, and Sella Nevo. Hydrology and Earth Systems Science (HESS), 2022.
Deep learning rainfall-runoff predictions of extreme events, Jonathan Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan Qualls, Hoshin Gupta, and Grey S. Nearing. Hydrology and Earth Systems Science (HESS), 2022.
Accelerating Physics Simulations with TPUs: An Inundation Modeling Example, Damien Pierce, R. Lily Hu, Yusef Shafi, Anudhyan Boral, Vladimir Anisimov, Sella Nevo, Yi-fan Chen. International Journal of High Performance Computing Applications (IJHPCA), 2022.