Matrix Completion for Storm Damages Prediction.

Forecasting weather disasters is very important, but still remains a big challenge for science. Aiming to tackle this issue, our study attempts to predict storm damages by using Semantic Web data (SRBench) and techniques (matrix completion methods and Statistical Unit Node Set framework). Preliminary experiments try predicting which regions are likely to be hit by the most deadly storms like hurricane Katrina (in USA, 2005). Result shows that, even with incomplete data, the approach can determine highly threatened locations at different timesteps. It also hints the ability to forecast different storm damage scenarios.