Advanced Machine Learning Methods for Major Hurricane Forecasting
Hurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the worst natural disasters in the 21st century. Further studies integrating the changing hurricane features are thus crucial to aid in the prediction of major hurricanes. With this in mind, we present a new fram...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/1/119 |
| _version_ | 1797431300305453056 |
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| author | Javier Martinez-Amaya Cristina Radin Veronica Nieves |
| author_facet | Javier Martinez-Amaya Cristina Radin Veronica Nieves |
| author_sort | Javier Martinez-Amaya |
| collection | DOAJ |
| description | Hurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the worst natural disasters in the 21st century. Further studies integrating the changing hurricane features are thus crucial to aid in the prediction of major hurricanes. With this in mind, we present a new framework based on automated decision tree analysis, which has the capability to identify the most important cloud structural parameters from GOES imagery as predictors for hurricane intensification potential in the Atlantic and Pacific oceans. The proposed framework has been proved effective for predicting major hurricanes with an overall accuracy of 73% from 6 to 54 h in advance (both regions combined). |
| first_indexed | 2024-03-09T09:41:55Z |
| format | Article |
| id | doaj.art-ac8a3826a1a94a62a20b167a2887d13d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:41:55Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ac8a3826a1a94a62a20b167a2887d13d2023-12-02T00:51:01ZengMDPI AGRemote Sensing2072-42922022-12-0115111910.3390/rs15010119Advanced Machine Learning Methods for Major Hurricane ForecastingJavier Martinez-Amaya0Cristina Radin1Veronica Nieves2Image Processing Laboratory, University of Valencia, 46980 Valencia, SpainImage Processing Laboratory, University of Valencia, 46980 Valencia, SpainImage Processing Laboratory, University of Valencia, 46980 Valencia, SpainHurricanes, rapidly increasing in complexity and strength in a warmer world, are one of the worst natural disasters in the 21st century. Further studies integrating the changing hurricane features are thus crucial to aid in the prediction of major hurricanes. With this in mind, we present a new framework based on automated decision tree analysis, which has the capability to identify the most important cloud structural parameters from GOES imagery as predictors for hurricane intensification potential in the Atlantic and Pacific oceans. The proposed framework has been proved effective for predicting major hurricanes with an overall accuracy of 73% from 6 to 54 h in advance (both regions combined).https://www.mdpi.com/2072-4292/15/1/119tropical cyclonessevere hurricanesrapid intensificationmachine learninghybrid modelingforecasting |
| spellingShingle | Javier Martinez-Amaya Cristina Radin Veronica Nieves Advanced Machine Learning Methods for Major Hurricane Forecasting Remote Sensing tropical cyclones severe hurricanes rapid intensification machine learning hybrid modeling forecasting |
| title | Advanced Machine Learning Methods for Major Hurricane Forecasting |
| title_full | Advanced Machine Learning Methods for Major Hurricane Forecasting |
| title_fullStr | Advanced Machine Learning Methods for Major Hurricane Forecasting |
| title_full_unstemmed | Advanced Machine Learning Methods for Major Hurricane Forecasting |
| title_short | Advanced Machine Learning Methods for Major Hurricane Forecasting |
| title_sort | advanced machine learning methods for major hurricane forecasting |
| topic | tropical cyclones severe hurricanes rapid intensification machine learning hybrid modeling forecasting |
| url | https://www.mdpi.com/2072-4292/15/1/119 |
| work_keys_str_mv | AT javiermartinezamaya advancedmachinelearningmethodsformajorhurricaneforecasting AT cristinaradin advancedmachinelearningmethodsformajorhurricaneforecasting AT veronicanieves advancedmachinelearningmethodsformajorhurricaneforecasting |