Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy Subject The topic of the resource Artificial intelligence; De-correlated neural network; Ensemble learning; Negative correlation learning; Predictive modeling Description An account of the resource Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Ni Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggestedstudy concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictorsas mentioned above. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. Creator An entity primarily responsible for making the resource Dash Y.; Abraham A.; Kumar N.; Yadav D.K.; Singhal N. Source A related resource from which the described resource is derived Earth Science Informatics, Vol-18, No. 1 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2025-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s12145-024-01532-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s12145-024-01532-y</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213963871&amp;doi=10.1007%2Fs12145-024-01532-y&amp;partnerID=40&amp;md5=836c48c60adcd7aeb8fdf221e58f97e8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213963871&amp;doi=10.1007%2fs12145-024-01532-y&amp;partnerID=40&amp;md5=836c48c60adcd7aeb8fdf221e58f97e8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18650473 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dash Y., School of Artificial Intelligence, Bennett University, Greater Noida, India; Abraham A., School of Artificial Intelligence, Bennett University, Greater Noida, India; Kumar N., SCSET, Bennett University, Greater Noida, India; Yadav D.K., SCSET, Bennett University, Greater Noida, India; Singhal N., Christ University, Central Campus, Bangaluru, India