Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning
This study evaluates the ability of three machine learning methods—decision tree classifier (DTC), random forest classifier (RFC), and XGBoost classifier (XGBC)—to classify and predict tropical cyclone (TC) rapid intensification (RI) and non-RI over the Southwest Pacific Ocean basin (SWPO) from 1982...
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MDPI AG
2025-04-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/4/456 |
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| author | Rupsa Bhowmick |
| author_facet | Rupsa Bhowmick |
| author_sort | Rupsa Bhowmick |
| collection | DOAJ |
| description | This study evaluates the ability of three machine learning methods—decision tree classifier (DTC), random forest classifier (RFC), and XGBoost classifier (XGBC)—to classify and predict tropical cyclone (TC) rapid intensification (RI) and non-RI over the Southwest Pacific Ocean basin (SWPO) from 1982 to 2023. Among the 324 TCs within the domain, 81 were identified as RI TCs, exhibiting a 24-h intensity increase of at least 15 ms<sup>−1</sup> at least once in their lifetime. Environmental variables used for the input matrix are extracted from the nearest grid cell corresponding to each RI and non-RI event’s geographic location and time of occurrence. Additionally, the geographic location of each event and its initial intensity positions (24-h prior) are also included in the model. The XGBC, with 10-fold cross-validation, became the optimum classifier by achieving the highest classification accuracy, as well as the lowest probability of false detection and the highest AUC score on the unseen data. The model identified the longitude of RI and non-RI events, initial intensity latitude, extent of initial intensity, and relative humidity at 850 hPa as the most important variables in the classification decision. This study will advance storm preparedness strategies for the SWPO nations through correctly predicting RI-TCs and prioritizing early prediction of contributing environmental variables. |
| format | Article |
| id | doaj-art-e151e2b5319440b0b3086ae424d7d6eb |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-e151e2b5319440b0b3086ae424d7d6eb2025-08-20T03:14:16ZengMDPI AGAtmosphere2073-44332025-04-0116445610.3390/atmos16040456Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine LearningRupsa Bhowmick0Department of Geography and Environmental Science, University of Wisconsin—La Crosse, La Crosse, WI 54601, USAThis study evaluates the ability of three machine learning methods—decision tree classifier (DTC), random forest classifier (RFC), and XGBoost classifier (XGBC)—to classify and predict tropical cyclone (TC) rapid intensification (RI) and non-RI over the Southwest Pacific Ocean basin (SWPO) from 1982 to 2023. Among the 324 TCs within the domain, 81 were identified as RI TCs, exhibiting a 24-h intensity increase of at least 15 ms<sup>−1</sup> at least once in their lifetime. Environmental variables used for the input matrix are extracted from the nearest grid cell corresponding to each RI and non-RI event’s geographic location and time of occurrence. Additionally, the geographic location of each event and its initial intensity positions (24-h prior) are also included in the model. The XGBC, with 10-fold cross-validation, became the optimum classifier by achieving the highest classification accuracy, as well as the lowest probability of false detection and the highest AUC score on the unseen data. The model identified the longitude of RI and non-RI events, initial intensity latitude, extent of initial intensity, and relative humidity at 850 hPa as the most important variables in the classification decision. This study will advance storm preparedness strategies for the SWPO nations through correctly predicting RI-TCs and prioritizing early prediction of contributing environmental variables.https://www.mdpi.com/2073-4433/16/4/456tropical cyclonerapid intensificationclassificationmachine learningSouthwest Pacific Ocean basin |
| spellingShingle | Rupsa Bhowmick Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning Atmosphere tropical cyclone rapid intensification classification machine learning Southwest Pacific Ocean basin |
| title | Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning |
| title_full | Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning |
| title_fullStr | Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning |
| title_full_unstemmed | Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning |
| title_short | Southwest Pacific Tropical Cyclone Rapid Intensification Classification Utilizing Machine Learning |
| title_sort | southwest pacific tropical cyclone rapid intensification classification utilizing machine learning |
| topic | tropical cyclone rapid intensification classification machine learning Southwest Pacific Ocean basin |
| url | https://www.mdpi.com/2073-4433/16/4/456 |
| work_keys_str_mv | AT rupsabhowmick southwestpacifictropicalcyclonerapidintensificationclassificationutilizingmachinelearning |