Science journalist David Hambling has covered the hurricane modeling research of SMU engineers Yu Su, Michael Hahsler and Margaret Dunham in the U.K. daily newspaper The Guardian. The article published in Hambling’s Oct. 12 column “Weatherwatch.”
Su, Hahsler and Dunham have written a white paper on their method for predicting hurricanes: “Learning a Prediction Interval Model for Hurricane Intensities.” The three scientists are in the SMU Lyle School‘s Department of Computer Science and Engineering.
By David Hambling
It is possible to predict the track of a hurricane with a reasonable degree of accuracy several days in advance. Unfortunately predicting intensity is less certain, and potential victims don’t know whether to expect a rather heavy thunderstorm or something truly apocalyptic. Evacuation may be a wise precautionary measure, but when the promised devastation does not occur it looks like crying wolf.
Researchers at the Southern Methodist University in Dallas, Texas are developing a new modelling technique to predict the speed of hurricane winds. Known as the Learning Prediction Intensity Interval model, it is based on data mining using an advanced machine learning process. The computer itself works out the pattern of intensity development from a large pool of raw data, unlike existing methods where humans cherry-pick the most relevant historical data for a regression model to fit the current situation.