The next chapter in flood resilience: Open sourcing Google’s hydrology framework

We have open-sourced our hydrology model to enable National Meteorological and Hydrological Services to integrate advanced AI-based flood forecasting into their own workflows.

Floods are one of the most devastating natural hazards worldwide, often arriving with little warning and leaving long-term damage. Over several years, Google Research has built state-of-the-art AI models for more accurate flood forecasting, ensuring this technology reaches frontline responders to give them time to act. To help further protect vulnerable communities, we are now open-sourcing our hydrology modeling framework on GitHub for others to use and build upon.

This open source modeling framework allows researchers and forecasters to train AI flood forecasting models with the same architecture and similar training data to what is used to power riverine flood forecasts on Google’s Flood Hub. It is developed to allow hydrological scientists to build on what we have done at Google Research by adding and testing new models, data, and approaches. It also allows operational forecasters — people whose job entails providing actionable flood warnings for specific areas — to incorporate local data and knowledge into state-of-the-art AI-based flood forecasting. This initiative joins the Google Earth AI family of geospatial models and datasets, reinforcing our commitment to scientific leadership in crisis resilience.

How it works

Our hydrology model is a Python package that uses the open source PyTorch machine learning modeling package to implement the river forecast model that drives the Google Flood Hub. These models take input data in the form of geographical features related to climate, soils, topography, and land cover, along with meteorological forecasts related to rainfall, temperature, and other weather conditions to predict the daily flow rate of rivers around the world.