Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method
Tropical cyclones (TC) are extreme weather conditions caused by severe circular storms that originate in warm oceans. They are strong destructive forces that cause disastrous effects on human life and property and lead to economic damage. Therefore, it is necessary to forecast the TC intensity to av...
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| Format: | Article |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10203006/ |
| _version_ | 1797742959242772480 |
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| author | J. Senthil Kumar V. Venkataraman S. Meganathan Kannan Krithivasan |
| author_facet | J. Senthil Kumar V. Venkataraman S. Meganathan Kannan Krithivasan |
| author_sort | J. Senthil Kumar |
| collection | DOAJ |
| description | Tropical cyclones (TC) are extreme weather conditions caused by severe circular storms that originate in warm oceans. They are strong destructive forces that cause disastrous effects on human life and property and lead to economic damage. Therefore, it is necessary to forecast the TC intensity to avoid the issues. This study proposes a TC intensity forecast using Long-Short Term Memory (LSTM) with Cat Swarm Optimization (CSO). The LSTM method was optimized using the Cat Swarm Optimization technique to improve accuracy and reduce prediction errors. In this study, the prediction was carried out using the latitude, longitude, pressure, and wind speed of tropical cyclones from 2003 to 2019 in the Bay of Bengal. The performance of the proposed system was evaluated using the performance metrics, such as accuracy, Root Mean Square Error (RMSE), Average Absolute Position Error, Mean Absolute Error (MAE), and Area Under Receiver Operating Characteristic Curve (AUROC). The performance of the proposed system is compared with the results of other traditional methods, and the results show that the LSTM-CSO method outperforms other methods in TC intensity and track prediction. |
| first_indexed | 2024-03-12T14:47:41Z |
| format | Article |
| id | doaj.art-41887da2726640a4b89e3ca77cceb378 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:47:41Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-41887da2726640a4b89e3ca77cceb3782023-08-15T23:01:33ZengIEEEIEEE Access2169-35362023-01-0111816138162210.1109/ACCESS.2023.330133110203006Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO MethodJ. Senthil Kumar0https://orcid.org/0000-0002-9226-9773V. Venkataraman1S. Meganathan2Kannan Krithivasan3Department of Computer Science, Srinivasan Ramanujan Center, SASTRA Deemed University, Thanjavur, Tamil Nadu, IndiaDepartment of Mathematics, SASTRA Deemed University, Thanjavur, Tamil Nadu, IndiaDepartment of Computer Science, Srinivasan Ramanujan Center, SASTRA Deemed University, Thanjavur, Tamil Nadu, IndiaDepartment of Mathematics, SASTRA Deemed University, Thanjavur, Tamil Nadu, IndiaTropical cyclones (TC) are extreme weather conditions caused by severe circular storms that originate in warm oceans. They are strong destructive forces that cause disastrous effects on human life and property and lead to economic damage. Therefore, it is necessary to forecast the TC intensity to avoid the issues. This study proposes a TC intensity forecast using Long-Short Term Memory (LSTM) with Cat Swarm Optimization (CSO). The LSTM method was optimized using the Cat Swarm Optimization technique to improve accuracy and reduce prediction errors. In this study, the prediction was carried out using the latitude, longitude, pressure, and wind speed of tropical cyclones from 2003 to 2019 in the Bay of Bengal. The performance of the proposed system was evaluated using the performance metrics, such as accuracy, Root Mean Square Error (RMSE), Average Absolute Position Error, Mean Absolute Error (MAE), and Area Under Receiver Operating Characteristic Curve (AUROC). The performance of the proposed system is compared with the results of other traditional methods, and the results show that the LSTM-CSO method outperforms other methods in TC intensity and track prediction.https://ieeexplore.ieee.org/document/10203006/Tropical cyclonehyper-parameter tuningcat swarm optimizationlong-short term memorypressurewind speed |
| spellingShingle | J. Senthil Kumar V. Venkataraman S. Meganathan Kannan Krithivasan Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method IEEE Access Tropical cyclone hyper-parameter tuning cat swarm optimization long-short term memory pressure wind speed |
| title | Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method |
| title_full | Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method |
| title_fullStr | Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method |
| title_full_unstemmed | Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method |
| title_short | Tropical Cyclone Intensity and Track Prediction in the Bay of Bengal Using LSTM-CSO Method |
| title_sort | tropical cyclone intensity and track prediction in the bay of bengal using lstm cso method |
| topic | Tropical cyclone hyper-parameter tuning cat swarm optimization long-short term memory pressure wind speed |
| url | https://ieeexplore.ieee.org/document/10203006/ |
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