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 Faculty Publications Conference Paper Faculty Publications- Conference Papers 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/. Creator An entity primarily responsible for making the resource Anushka; George, Jossy; Nair, Akhil M.; Alapatt, Bosco Paul; Baby, Riya; Jose, Jiby Title A name given to the resource An Efficient Machine Learning Framework for Flood Forecasting Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1278;pp.433-444 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2703-5_29" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2703-5_29</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105006929995?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006929995?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Anushka, School of Sciences, Christ University, Bengaluru, India; George J., School of Sciences, Christ University, Bengaluru, India; Nair A.M., Luxsh Technologies Pvt Ltd, Northwood Hills, Middlesex, United Kingdom; Alapatt B.P., School of Sciences, Christ University, Bengaluru, India; Baby R., School of Sciences, Christ University, Bengaluru, India; Jose J., School of Sciences, Christ University, Bengaluru, India Description An account of the resource Floods, a significant natural disaster which has an impact on the whole world present major risks to ecosystems and humans, particularly in semi-arid areas with variable rainfall patterns. With the help of ICRISATs historical meteorological data and machine learning algorithms, this study has developed a customized flood prediction model for use. After evaluating and contrasting various models, including the proposed model Stacked Gradient Boosting with Random Forest (SGB-RAF), KNN, Decision Tree, Random Forest, and Linear Regression, it shows that SGB-RAF has the highest R2 score and lowest RMSE comparatively to other models. While other enhancements such as Ridge Regression and polynomial feature creation were studied, SGB-RAF remained effective. Overall, this study highlights how machine learning may improve flood prediction accuracy, which is important for disaster management and for improving the semi-arid regions adaptability to climatic variability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Decision tree; Flood prediction; KNN; Logistic regression; Machine learning; Random forest; Support vector regression Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981962702-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online