Artificial intelligence is increasingly being used to improve flood forecasting and early warning systems, helping authorities and communities get more information about potential flooding before it happens.
The World Meteorological Organization (WMO) says AI can strengthen forecasts and early warnings by processing information quickly, combining different data sources and identifying complex patterns. In hydrology, AI and machine learning can support river flood forecasting and help improve decisions related to water resources.
AI helps detect flood risks earlier
Traditional flood forecasting systems use weather observations, river measurements and mathematical models to estimate the likelihood of flooding. AI can complement these systems by analysing large amounts of information and identifying patterns that may indicate an increased flood risk.
According to the WMO, artificial intelligence and machine learning are being explored for river flood forecasting as well as other weather-related hazards. The technology can help forecasting agencies process information more efficiently and strengthen early-warning capabilities.
Google uses AI for flood forecasting
Google has also developed AI-based technology for flood forecasting through its Flood Hub platform.
According to Google Research, Flood Hub provides real-time flood maps, water trends and forecasts based on AI models and global data sources. The platform can provide forecasts for riverine floods up to seven days in advance in supported areas, while its urban flash-flood forecasting system can provide predictions up to 24 hours ahead.
The forecasts are designed to help governments, aid organizations and communities assess flood risks and take action before dangerous conditions develop.
AI combines different sources of information
One of the advantages of AI-based forecasting is its ability to process different types of information together.
Flood forecasting systems can use meteorological forecasts, historical observations, river information and other geographical data to estimate the likelihood of flooding. Google says its flood forecasting models use AI and hydrological data to generate forecasts, including in locations where traditional measurement infrastructure may be limited.
The WMO also highlights the importance of integrating AI with existing observation and forecasting systems rather than treating it as a replacement for established meteorological services.
AI flood forecasting is being tested internationally
AI-powered flood forecasting is already being tested in different countries.
The WMO has reported pilot projects in Nigeria, Viet Nam, Uruguay and Czechia, where AI-based systems have helped identify early signals that could otherwise have been missed. These systems are being explored as part of wider efforts to strengthen early warnings and give communities more time to respond to flood risks.
The technology is also being developed alongside broader international efforts to improve early-warning systems for weather and climate-related hazards.
AI does not replace human forecasting
Despite advances in artificial intelligence, AI systems are not intended to completely replace traditional forecasting methods or expert meteorologists and hydrologists.
Instead, AI can work alongside existing forecasting infrastructure to process information, identify patterns and provide additional predictions. Official agencies remain important for assessing forecasts and communicating warnings to the public.
Earlier warnings could save lives
The biggest potential benefit of AI-powered flood forecasting is additional preparation time.
Earlier and more accurate information can help emergency services prepare for flooding, allow authorities to plan evacuations and give communities more time to protect people and property.
As AI models continue to improve, their integration with weather observations, satellite data and hydrological systems could make flood forecasting and early-warning services faster and more accessible.
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