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Predicting Meteorological Data using Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 9 |
| Year of Publication: 2025 |
| Authors: Syed Hamid Ali Shah, Rameez Asif |
10.5120/ijca2025925007
|
Syed Hamid Ali Shah, Rameez Asif . Predicting Meteorological Data using Machine Learning. International Journal of Computer Applications. 187, 9 ( May 2025), 23-29. DOI=10.5120/ijca2025925007
Abstract
Understanding the nature and behavior of weather has always been an essential task for humans, as it has a significant impact on the property and economy of a country. Machine Learning algorithms can predict the patterns of the weather nature i.e. floods, hurricanes, storms, cyclones, and rain. This paper looks at applying machine learning techniques to predict target variables i.e. precipitation to independent variables i.e. wind speed, temperature, pressure, and soil temperature for three years of weather data collected from the Manchester region that is publicly available on the OpenMeteo website. The objective of this research is to evaluate and compare the accuracy, precision, F1 score, and recall of linear regression, support vector machine regression, k-nearest neighbor regression, and random forest regression algorithms using Python,starting with preprocessing the data, developing the algorithms, training, and finally testing it. The low mean squared error (MSE), R² score, and mean absolute error (MAE) illustrate the ability of these algorithms for prediction. Further analysis of these algorithms shows linear and support vector machine regression with 92.2% and 92.5% accuracy.
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