Forecasting meteorological analysis using machine learning algorithms.

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

Abstract

Weather prediction is gaining up ubiquity quickly in the current period of Machine learning and Technologies. It is fundamental to foresee the temperature of the climate for quite a while. Decision trees, K-NN, Random Forest algorithms are an integral asset which has been utilized in several prediction works for instance, flood prediction, storm detection etc. In this paper, a simple approach for weather prediction of future years by utilizing the past data analysis is proposed by the decision tree, K-NN and random forest algorithm calculations and showing the best accuracy result of these three algorithms. Weather prediction plays a significant job in everyday applications and in this paper the prediction is done based on the temperature changes of the certain area. All these algorithms calculate the mean values, median, confidence values, probability and show the difference between plots of all the three algorithms etc. Finally, using these algorithms in this work we can predict whether the temperature increases or decreases, is it a rainy day or not. The dataset is completely based on the weather of certain area including few objects like year, month, and temperature, predicted values and so on..

Citation

PAVULURI, B.L., VEJENDLA, R.S., JITHENDRA, P., DEEPIKA, T. and BANO, S. 2020. Forecasting meteorological analysis using machine learning algorithms. In Proceedings of the 2020 International conference on smart electronics and communication (ICOSEC 2020), 10-12 September 2020, Trichy, India. Piscataway: IEEE [online], pages 456-461. Available from: https://doi.org/10.1109/ICOSEC49089.2020.9215440

• Conference Name: 2020 International conference on smart electronics and communication (ICOSEC 2020)
• Conference Location: Trichy, India
• Start Date: Sep 10, 2020
• End Date: Sep 12, 2020
• Acceptance Date: Aug 10, 2020
• Online Publication Date: Oct 7, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Sep 20, 2023
• Publicly Available Date: Sep 20, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 456-461
• ISBN: 9781728154626
• DOI: https://doi.org/10.1109/ICOSEC49089.2020.9215440
• Keywords: Decision trees; Random forest algorithms; Weather prediction; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2064082