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 Evangilin, K.; Ryan Powell, L.; Suganthi, J.; Deepa, S.; Siddalingappa, Rashmi Title A name given to the resource Comparative Analysis of Machine Learning Models and Interpolation Techniques for Seasonal Rainfall Prediction in Tamil Nadu 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 Proceedings of 2025 International Conference on Emerging Trends in Industry 4.0 Technologies, ICETI4T 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICETI4T63625.2025.11131696" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICETI4T63625.2025.11131696</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016417680?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016417680?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 Evangilin K., Christ (Deemed to be University), Department of Data Science and Statistics, Bangalore, India; Ryan Powell L., Christ (Deemed to be University), Department of Data Science and Statistics, Bangalore, India; Suganthi J., Christ (Deemed to be University), Department of Data Science and Statistics, Bangalore, India; Deepa S., Christ (Deemed to be University), Department of Data Science and Statistics, Bangalore, India; Siddalingappa R., Christ (Deemed to be University), Department of Science and Statistics, Bangalore, India Description An account of the resource This paper explains the rainfall patterns in the state of Tamil Nadu in October 2024, which is the monsoon season, with respect to the differences between the actual rainfall and what is experienced normally over districts. This study uses machine learning techniques from regression models of Random Forest and Gradient Boosting to anticipate future trends about rainfall based on the precedent data. Evaluation using Performance Metrics. The Proposed models are very well tested in terms of performance metrics like RMSE and R-squared, which gives insight about how accurate the forecasts of their results are. This research shows the applicability of QGIS to achieve geospatial analysis for visualizations of the rain distribution as well as anomalies across districts. The current work depicts the integration of data science methodology with geospatial analysis into the knowledge about climate dynamics in the state of Tamil Nadu. Research will help in deepening the understanding of regional climate impacts by bridging predictive analytics with spatial visualization, lending support to informed decision-making in the environment management context. 2025 IEEE. Subject The topic of the resource Climate Dynamics; Geospatial Analysis; Gradient Boosting; Machine Learning; Predictive Modelling; QGIS; Rainfall Patterns; Random Forest; Tamil Nadu Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150696-4; 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