ItemConference Paper

Comparative Analysis of Machine Learning Models and Interpolation Techniques for Seasonal Rainfall Prediction in Tamil Nadu

Title
Comparative Analysis of Machine Learning Models and Interpolation Techniques for Seasonal Rainfall Prediction in Tamil Nadu
Creator
Evangilin, K.; Ryan Powell, L.; Suganthi, J.; Deepa, S.; Siddalingappa, Rashmi
Description
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.
Source
Proceedings of 2025 International Conference on Emerging Trends in Industry 4.0 Technologies, ICETI4T 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Climate Dynamics; Geospatial Analysis; Gradient Boosting; Machine Learning; Predictive Modelling; QGIS; Rainfall Patterns; Random Forest; Tamil Nadu
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833150696-4;
Format
online
Language
English
Type
Conference paper

Citation

Evangilin, K.; Ryan Powell, L.; Suganthi, J.; Deepa, S.; Siddalingappa, Rashmi, “Comparative Analysis of Machine Learning Models and Interpolation Techniques for Seasonal Rainfall Prediction in Tamil Nadu,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26002.

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