Tropical cyclone intensity estimations over the Indian ocean using Machine Learning

TL;DR

This study employs machine learning algorithms to accurately estimate tropical cyclone intensity and maximum sustained surface wind speed over the North Indian Ocean, aiding in disaster preparedness and response.

Contribution

It introduces a machine learning-based approach for cyclone intensity estimation using multiple attributes, achieving high accuracy and low error rates.

Findings

Cyclone grade estimation accuracy of 88%, up to 98.84% for higher categories.

MSWS estimation with RMSE of 2.3 overall, 2.2-3.4 for recent cyclones.

Models validated with recent cyclone data, showing high predictive performance.

Abstract

Tropical cyclones are one of the most powerful and destructive natural phenomena on earth. Tropical storms and heavy rains can cause floods, which lead to human lives and economic loss. Devastating winds accompanying cyclones heavily affect not only the coastal regions, even distant areas. Our study focuses on the intensity estimation, particularly cyclone grade and maximum sustained surface wind speed (MSWS) of a tropical cyclone over the North Indian Ocean. We use various machine learning algorithms to estimate cyclone grade and MSWS. We have used the basin of origin, date, time, latitude, longitude, estimated central pressure, and pressure drop as attributes of our models. We use multi-class classification models for the categorical outcome variable, cyclone grade, and regression models for MSWS as it is a continuous variable. Using the best track data of 28 years over the North Indian Ocean, we estimate grade with an accuracy of 88% and MSWS with a root mean square error (RMSE) of 2.3. For higher grade categories (5-7), accuracy improves to an average of 98.84%. We tested our model with two recent tropical cyclones in the North Indian Ocean, Vayu and Fani. For grade, we obtained an accuracy of 93.22% and 95.23% respectively, while for MSWS, we obtained RMSE of 2.2 and 3.4 and $R^2$ of 0.99 and 0.99, respectively.