Floods are one of the most common natural disasters, causing widespread devastation and loss of life. In areas with limited data, traditional flood forecasting methods are often inaccurate. This is where AI comes in.
Researchers at Google AI have developed a new machine learning (ML) model that can predict floods more accurately than ever before. The model, which is based on a type of artificial neural network called a Long Short-Term Memory (LSTM) network, can learn from historical data to identify patterns that are indicative of floods.
The new model has been incorporated into a flood forecasting system called the Flood Hub. The Flood Hub provides real-time flood forecasts up to seven days in advance. This information can be used by communities to prepare for floods and save lives.
LSTM networks for flood forecasting
LSTM networks are a type of artificial neural network that are well-suited for tasks that involve sequential data, such as time series forecasting. In the case of flood forecasting, the LSTM network can learn from historical data of river levels, rainfall, and other factors to predict future flood events.
The use of AI in flood forecasting is a promising development that has the potential to save lives. By providing more accurate and timely flood forecasts, AI can help communities to prepare for floods and reduce the risk of damage and loss of life.
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