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 Shaw, Ayush; Shoran, Preety; Vidushi Title A name given to the resource A Comparative Study of Machine Learning Models for Predicting Earthquake Magnitudes 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 International Conference on Engineering, Technology and Management, ICETM 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICETM63734.2025.11051600" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICETM63734.2025.11051600</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012020590?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012020590?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 Shaw A., Christ (Deemed to be University), Bangalore, Department of Computer Sciences, Delhi, India; Shoran P., Christ (Deemed to be University), Bangalore, Department of Computer Sciences, Delhi, India; Vidushi, Christ (Deemed to be University), Bangalore, Department of Computer Sciences, Delhi, India Description An account of the resource This study evaluates the effectiveness of machine learning models in predicting earthquake magnitudes, aiming to address the challenges posed by traditional seismological methods. By leveraging geospatial and temporal data, the research compares the performance of Random Forest (RF), Artificial Neural Network (ANN), and K-Nearest Neighbours (KNN) using Mean Absolute Error (MAE) as the primary metric. Random Forest demonstrated the lowest MAE of 0.3291, showcasing its ability to handle complex, non-linear patterns better than ANN (0.3660) and KNN (0.3758). The analysis highlights the importance of geospatial and temporal factors in improving prediction accuracy, offering insights into their predictive significance. While traditional methods struggle with high-dimensional data, this study eliminates these limitations by employing machine learning models capable of extracting meaningful patterns. These findings underscore the potential of ensemble methods like Random Forest for enhancing earthquake prediction systems. Future research will explore hybrid approaches and real-time data integration to further advance predictive accuracy in seismology. 2025 IEEE. Subject The topic of the resource Earthquake Prediction; K-Nearest Neighbours; Machine Learning; Magnitude Prediction; Temporal and Geospatial Data Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156668-5; 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