ThermAI: Exploring Temperature Analysis Through Diverse Machine Learning Models
Machine learning methods; Meteorological forecasting; Random forests; Temperature predictions
Meteorological forecasting is crucial in multiple industries, including agriculture, aviation, and daily routines. The objective of this inquiry is to improve temperature predictions by examining and comparing several machine learning methods, such as linear regression, decision trees, and random forests. This work aims to fill the gap in assessing machine learning models for temperature forecasting on a broader scale by utilising the comprehensive Indian meteorological dataset, which covers a wide range of geographical regions. The research utilises a thorough technique that includes gathering data, selecting relevant features, choosing appropriate models, and evaluating the results using R-squared and Mean Square Error metrics. The findings demonstrate that the Random Forest model surpasses both multiple linear regression and decision trees in terms of performance, displaying superior accuracy and reduced prediction errors. This study enhances proactive weather management and decision-making processes by offering valuable insights and tools to stakeholders in various industries. The work is organised into distinct sections that encompass a literature review, methodology, results, and conclusions, providing a comprehensive viewpoint on developments in temperature forecasting. 2024 IEEE.
1st International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICECCC61767.2024.10593875 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200676343&doi=10.1109%2fICECCC61767.2024.10593875&partnerID=40&md5=f800e9b2b92ef9f1d9ee6fc790fc086a
Thomas D.R.B., CHRIST University, Department of computer science, Bangaluru, India; Joy H.K., CHRIST University, Department of computer science, Bangaluru, India