Weather event severity prediction using buoy data and machine learning
University of California, Berkeley（加利福尼亚大学伯克利分校）
英文摘要：
In this paper, we predict severity of extreme weather events (tropical storms, hurricanes, etc.) using buoy data time series variables such as wind speed and air temperature. The prediction/forecasting method is based on various forecasting and machine learning models. The following steps are used. Data sources for the buoys and weather events are identified, aggregated and merged. For missing data imputation, we use Kalman filters as well as splines for multivariate time series. Then, statistical tests are run to ascertain increasing trends in weather event severity. Next, we use machine learning to predict/forecast event severity using buoy variables, and report good accuracies for the models built.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
