Weather event severity prediction using buoy data and machine learning

TL;DR

This paper develops machine learning models to predict the severity of extreme weather events like hurricanes using buoy data, employing data preprocessing, trend analysis, and forecasting techniques to achieve accurate predictions.

Contribution

It introduces a comprehensive approach combining data merging, imputation, statistical trend testing, and machine learning for weather severity prediction from buoy data.

Findings

Good prediction accuracies achieved with machine learning models.

Effective data imputation using Kalman filters and splines.

Statistical tests confirm increasing trends in weather severity.

Abstract

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.