Comparison of performances of regression model-based prediction of meteorological conditions.

Vejendla, Ramya Sree; Pavuluri, Bhagya Lakshmi; Venigandla, Nandini; Tinnavalli, Deepika; Bano, Shahana

Abstract

This paper clearly illustrates the working of different machine learning algorithms to determine the weather conditions. This involves the prediction of temperature by training with the pre-existing dataset of weather conditions on each day for around 40 years. This trained data is tested to evaluate the temperature of a certain day in the upcoming calendar by date or year. This illustration describes the comparison between different algorithm results and determines the most efficient algorithm. The algorithm involved were Linear Regression, Logistic Regression, and Clustering. These three algorithms involve different mechanisms such as predicting based on mean, probability, and grouping based on similar constraints. The model helps to select the most efficient algorithm which gives the approximate values nearer to accurate values. Though all the techniques involved in previous analysis are mostly based on mean analysis the result is almost approximate but under logistic regression, it either gives almost the accurate result or the wrong result. Here we introduce clustering since the date or year could be grouped under a certain condition where either based on the temperature of a certain year or the season.

Citation

VEJENDLA, R.S., PAVULURI, B.L., VENIGANDLA, N., TINNAVALLI, D. and BANO, S. 2022. Comparison of performances of regression model-based prediction of meteorological conditions. In Kumar, A., Senatore, S. and Gunjan, V.K. (eds.) Proceedings of the 2nd International conference on data science, machine learning and applications (ICDSMLA 2020), 21-22 November 2020, Pune, India. Lecture notes in electrical engineering, 783. Singapore: Springer [online], pages 155-169. Available from: https://doi.org/10.1007/978-981-16-3690-5_15

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2nd International conference on data science, machine learning and applications (ICDSMLA 2020)
• Start Date: Nov 21, 2020
• End Date: Nov 22, 2020
• Acceptance Date: Oct 15, 2020
• Online Publication Date: Nov 9, 2021
• Publication Date: Dec 31, 2022
• Deposit Date: Nov 12, 2024
• Publicly Available Date: Nov 12, 2024
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 155-169
• Series Title: Lecture notes in electrical engineering
• Series Number: 783
• Series ISSN: 1876-1100; 1876-1119
• ISBN: 9789811636899
• DOI: https://doi.org/10.1007/978-981-16-3690-5_15
• Keywords: Weather prediction; Weather forecasting; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2063964