Tropical cyclone intensity estimations over the Indian ocean using Machine Learning

by   Koushik Biswas, et al.

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 2.3. For higher grade categories (5-7), accuracy improves to an average of 98.84 Indian Ocean, Vayu and Fani. For grade, we obtained an accuracy of 93.22 95.23 of 0.99 and 0.99, respectively.


page 6

page 8

page 9


Predicting wind pressures around circular cylinders using machine learning techniques

Numerous studies have been carried out to measure wind pressures around ...

Prediction of Landfall Intensity, Location, and Time of a Tropical Cyclone

The prediction of the intensity, location and time of the landfall of a ...

Detecting chaos in hurricane intensity

Determining the maximum potential limit in the accuracy of hurricane int...

Inference of Personal Attributes from Tweets Using Machine Learning

Using machine learning algorithms, including deep learning, we studied t...

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

Accurate measurement of pressure drop in energy sectors especially oil a...

Automated surface feature selection using SALSA2D: An illustration using Elephant Mortality data in Etosha National Park

This analysis is motivated by the MIKE dataset in Etosha National Park (...

Please sign up or login with your details

Forgot password? Click here to reset