Updated: Sep 22
(Picture Courtesy: Pinterest)
Google’s flood prediction service makes use of machine learning to identify areas of land prone to flooding and alert users before the waters arrive. This service now is available across all of India and parts of Bangladesh as well. Google estimates this service will help to protect above 200 million in India and about 40 million in Bangladesh.
Google’s AI Blog reveals this technology primarily uses four steps of modelling using advance AI techniques. The steps involved are:
The Inundation Model - A critical step in developing an accurate flood forecasting system is to develop inundation models, which use either a measurement or a forecast of the water level in a river as an input and simulate the water behavior across the floodplain.
Real-time Water Level Measurements - To run these models operationally, Google needs to know what is happening on the ground in real-time, and thus we rely on partnerships with the relevant government agencies to receive timely and accurate information. In this case its first governmental partner is the Indian Central Water Commission (CWC), which measures water levels hourly in over a thousand stream gauges across all of India, aggregates this data, and produces forecasts based on upstream measurements. The CWC provides these real-time river measurements and forecasts, which are then used as inputs for our models.
Elevation Map Creation –It is critical that these models have a good map of the terrain. High-resolution digital elevation models (DEMs) are incredibly useful for a wide range of applications in the earth sciences. This is achieved by a novel methodology to produce high resolution DEMs based on completely standard optical imagery.
· Hydraulic Modeling - Once the model has both these inputs - the riverine measurements and forecasts, and the elevation map - The first and most substantial component is the physics-based hydraulic model, which updates the location and velocity of the water through time based on (an approximated) computation of the laws of physics. In addition, there is a predictive inundation model, based on historical measurements.
(Picture Courtesy: Google AI blog)
Google says it’s experimenting with new models that can provide even more accurate alerts. Its latest forecast model can “double the lead time” of its previous system, says the company, while also providing people with information about the depths of the flooding. “In more than 90 percent of cases, our forecasts will provide the correct water level within a margin of error of 15 centimeters,” say Google’s researchers.
A study of Google’s forecasts in the Ganges-Brahmaputra river basin carried out with scientists from Yale found that 70 percent of people who received a flood alert did so before flood waters arrived, and 65 percent of households that received an alert took action. “Even in an area suffering from low literacy, limited education, and high poverty, a majority of citizens act on information they receive,” write the researchers. “So, early warnings are definitely worth the effort.”
Google AI blogs: An Inside Look at Flood Forecasting