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 Arora, Suchita; Kumar, Sunil; Kumar, Sandeep Title A name given to the resource Enhanced Random Forest-Based Model forFlood Detection andClassification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;122 SIST;pp.799-807 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-6250-0_55" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-6250-0_55</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023102818?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023102818?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 Arora S., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India, Department of Computer Science and Engineering, Poornima University, Rajasthan, Jaipur, 303905, India; Kumar S., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India; Kumar S., Department of AI, ML and Data Science, Christ University, Bangalore, India Description An account of the resource Flooding is one of the most devastating natural disasters globally, causing extensive damage to infrastructure, the environment, and human lives. With increasing occurrences due to climate change, accurate classification and analysis of flood imagery are essential for early detection, damage assessment, and post-disaster recovery. Reliable flood classification systems are critical for early warning, resource allocation, and mitigation efforts, helping to minimize the impact on affected regions. Remote sensing and computer vision techniques, including the Bag-of-Visual-Words (BOV) model, offer powerful tools for interpreting flood images by categorizing and identifying flooded regions across vast and complex terrains. This paper presents a modification of the standard Random Forest algorithm to enhance the accuracy of image classification within a Bag-of-Visual-Words (BOV) model. The modified Random Forest achieves better adaptability and performance across flood image datasets by introducing flexibility in parameter tuning through custom hyperparameters and automatic grid search. This modification addresses challenges in balancing efficiency and accuracy for classifying high-dimensional image data sets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Bag-of-visual-words; Flood detection; Natural disaster; Random Forest algorithm Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981966249-4; 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