2024
doi: 10.12720/jait.15.12.1329-1338
Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis
Adil Hussain1,*
,
Ayesha Aslam2 ,
Sajib Tripura1 ,
Vineet Dhanawat3 ,
Varun Shinde4
- 1School of Electronics and Control Engineering, Chang’an University, Xi’an, China
- 2School of Information Engineering, Chang’an University, Xi’an, China
- 3Meta Platforms Inc., California, United States
- 4Cloudera, Inc., Austin, Texas, United States
- Manuscript receivedJune 21, 2024
- revisedAugust 19, 2024
- acceptedSeptember 2, 2024
- publishedDecember 6, 2024
Abstract
Heavy rains result in significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended periods of intense precipitation. Accurate rainfall prediction is crucial in nations such as Bangladesh, where agriculture is the predominant field of occupation. The efficiency of machine learning methods is enhanced by the nonlinearity of rainfall, surpassing the effectiveness of other approaches. This study proposes the novel combination of rainfall occurrence prediction, rainfall amount prediction, and daily average temperature prediction. This research implements machine learning techniques and an ensemble-based classifier to predict rainfall occurrence, as well as machine learning regressor models and an ensemble-based regressor to predict the rainfall amount and daily average temperature, using the Bangladesh Weather Dataset. The ensemble classifier demonstrated an accuracy of 83.41% and a recall of 78.17%, exhibiting the best performance in predicting when it will rain, but its precision was the lowest, at 51.16%. The ensemble regression model outperformed the base models, including linear regression, random forest, and support vector regression in rainfall amount prediction, with the lowest mean absolute error of 0.36 and root mean squared error of 0.90. Additionally, this model provided the most precise daily average temperature prediction results with the lowest mean absolute error of 0.42 and root mean squared error of 0.54, highlighting its superiority over the other regression models in forecasting temperature. Ensemble approaches consistently exhibit superior task performance metrics.
Keywords
- rainfall prediction
- temperature prediction
- ensemble classifier
- rain prediction
- weather prediction
How to Cite
Adil Hussain, Ayesha Aslam, Sajib Tripura, Vineet Dhanawat, and Varun Shinde, "Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis," Journal of Advances in Information Technology, vol. 15, no. 12, pp. 1329-1338, 2024. https://doi.org/10.12720/jait.15.12.1329-1338
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
