Year: 2025
Type: article
Abstract: Tropical cyclones that occurred in the Northern Indian Ocean affected Myanmar Land.Historical datasets are obtained from Joint Typhoon Warning Center (JTWC) and provided from 1945 to 2022 years.Feature extraction has a critical role in machine learning theory.The movement of the cyclone trajectory points out the value of Latitude and Longitude.In this paper, these values are changing in the direction and magnitude of the movement.The main contribution is stand on the correlation coefficient value of the direction and magnitude of the historical trajectory data and test data.Not only the latitude and longitude of the cyclone but also metrological data such as wind speed and sea level pressure are also used the input data to extract the features.Features of Direction and Movement are extracted to build the model based on similar cyclones and tested one.Logistic regression method is used to forecast the latitude and longitude of a cyclone's location 24 hours ahead of time by using the last twelve hours of observations (two positions, at six hourly intervals, and the current position).The threshold value is also an essential decision-maker or forecaster of the system.According to the value, the accuracy of the system can also change.Three threshold values of the sigmoid function are tested which is based on two similar and three similar cyclones are tested.For the evaluation of the system, three matrixes are selected such as mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE).By adding a maximum wind speed and minimum sea level pressure from the historical dataset, performance evaluation is gradually improved for these regression methods.
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W4413234554