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Summary
A team of researchers from Ariel University and the Eastern R&D Center examined whether powerful flash floods could be predicted, up to 24 hours before they occur, by studying the changes in the precipitable water vapor in the atmosphere. In a paper published in IEEE Transactions on Geoscience and Remote Sensing, the scientists present the realistic potential of using the water vapor values in the air, calculated from nine ground GPS stations in the arid part of the Eastern Mediterranean region, to predict flash flood events.
Show Notes
A team of researchers from Ariel University and the Eastern R&D Center examined whether powerful flash floods could be predicted, up to 24 hours before they occur, by studying the changes in the precipitable water vapor in the atmosphere.
Flash floods are rapid, high-intensity flooding events that are caused mainly by heavy rainfall.
Since flashfloods have a short response time of several hours, they are difficult to predict and therefore often cause damages and even casualties.
Using machine learning methods applied to 24 hours of water vapor data, they were able to predict whether a flash flood will occur with over 90 percent accuracy.
They achieved even better results when additional measurements such as ground pressure measurements were added to the machine learning process.
Source
https://www.israel21c.org/flash-floods-could-be-predicted-with-machine-learning/
