More accurate method for predicting hurricane activity

Tue, 09/11/2012 - 7:37am

Researchers from North Carolina State University have developed a new method for forecasting seasonal hurricane activity that is 15% more accurate than previous techniques.

"This approach should give policymakers more reliable information than current state-of-the-art methods," says Nagiza Samatova, an associate professor of computer science at NC State and co-author of a paper describing the work. "This will hopefully give them more confidence in planning for the hurricane season."

Conventional models used to predict seasonal hurricane activity rely on classical statistical methods using historical data. Hurricane predictions are challenging, in part, because there are an enormous number of variables in play—such as temperature and humidity—which need to be entered for different places and different times. This means there are hundreds of thousands of factors to be considered.



The groups of important factors identified by the network motif-based model are then plugged into a program to create an ensemble of statistical models that present the hurricane activity for the forthcoming season on a probability scale. For example, it might say there is an 80% probability of high activity, a 15% probability of normal activity and a 5% probability of low activity.


Using cross validation—plugging in partial historical data and comparing the new method's results to subsequent historical events—the researchers found the new method has an 80% accuracy rate of predicting the level of hurricane activity. This compares to a 65% accuracy rate for traditional predictive methods.



The paper, "Discovery of extreme events-related communities in contrasting groups of physical system networks," was published online in Data Mining and Knowledge Discovery.

Source: North Carolina State University

Share This Story

The password field is case sensitive.