Skillful prediction of Indian Ocean Dipole index using machine learning models
In this study, we evaluated six machine learning models for their skill in predicting the Indian Ocean Dipole (IOD). The results based on the IOD index predictions at 1–8 month lead time indicate that the AdaBoost model with Multi-Layer Perceptron as the base estimator, AdaBoost(MLP), to perform bet...
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| Language: | English |
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Elsevier
2025-02-01
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| Series: | Applied Computing and Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197425000102 |
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| author | J.V. Ratnam Swadhin K. Behera Masami Nonaka Kalpesh R. Patil |
| author_facet | J.V. Ratnam Swadhin K. Behera Masami Nonaka Kalpesh R. Patil |
| author_sort | J.V. Ratnam |
| collection | DOAJ |
| description | In this study, we evaluated six machine learning models for their skill in predicting the Indian Ocean Dipole (IOD). The results based on the IOD index predictions at 1–8 month lead time indicate that the AdaBoost model with Multi-Layer Perceptron as the base estimator, AdaBoost(MLP), to perform better than the other five models in predicting the IOD index at all lead times. Interestingly, the IOD predictions of AdaBoost(MLP) had an anomaly correlation coefficient above 0.6 at almost all lead times. The results suggest that the AdaBoost(MLP) machine learning model to be a promising tool for predicting the IOD index with a long lead time of 8 months. Analysis revealed that the machine learning model predictions are aided by the signals from the Pacific region, owing to co-occurrences of some of the IODs with El Nino-Southern Oscillations. |
| format | Article |
| id | doaj-art-43fb90c505f0402da528cf797db2ed62 |
| institution | Kabale University |
| issn | 2590-1974 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj-art-43fb90c505f0402da528cf797db2ed622025-02-11T04:35:27ZengElsevierApplied Computing and Geosciences2590-19742025-02-0125100228Skillful prediction of Indian Ocean Dipole index using machine learning modelsJ.V. Ratnam0Swadhin K. Behera1Masami Nonaka2Kalpesh R. Patil3Corresponding author. 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 236-0001, Japan.; Application Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanApplication Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanApplication Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanApplication Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanIn this study, we evaluated six machine learning models for their skill in predicting the Indian Ocean Dipole (IOD). The results based on the IOD index predictions at 1–8 month lead time indicate that the AdaBoost model with Multi-Layer Perceptron as the base estimator, AdaBoost(MLP), to perform better than the other five models in predicting the IOD index at all lead times. Interestingly, the IOD predictions of AdaBoost(MLP) had an anomaly correlation coefficient above 0.6 at almost all lead times. The results suggest that the AdaBoost(MLP) machine learning model to be a promising tool for predicting the IOD index with a long lead time of 8 months. Analysis revealed that the machine learning model predictions are aided by the signals from the Pacific region, owing to co-occurrences of some of the IODs with El Nino-Southern Oscillations.http://www.sciencedirect.com/science/article/pii/S2590197425000102BoostingBootstrappingSSH |
| spellingShingle | J.V. Ratnam Swadhin K. Behera Masami Nonaka Kalpesh R. Patil Skillful prediction of Indian Ocean Dipole index using machine learning models Applied Computing and Geosciences Boosting Bootstrapping SSH |
| title | Skillful prediction of Indian Ocean Dipole index using machine learning models |
| title_full | Skillful prediction of Indian Ocean Dipole index using machine learning models |
| title_fullStr | Skillful prediction of Indian Ocean Dipole index using machine learning models |
| title_full_unstemmed | Skillful prediction of Indian Ocean Dipole index using machine learning models |
| title_short | Skillful prediction of Indian Ocean Dipole index using machine learning models |
| title_sort | skillful prediction of indian ocean dipole index using machine learning models |
| topic | Boosting Bootstrapping SSH |
| url | http://www.sciencedirect.com/science/article/pii/S2590197425000102 |
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