Version 1
: Received: 27 February 2024 / Approved: 27 February 2024 / Online: 27 February 2024 (17:50:13 CET)
Version 2
: Received: 12 September 2024 / Approved: 12 September 2024 / Online: 13 September 2024 (11:43:09 CEST)
How to cite:
Hussain, A.; Aslam, A.; Tripura, S.; Dhanawat, V.; Shinde, V. Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis. Preprints2024, 2024021566. https://doi.org/10.20944/preprints202402.1566.v2
Hussain, A.; Aslam, A.; Tripura, S.; Dhanawat, V.; Shinde, V. Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis. Preprints 2024, 2024021566. https://doi.org/10.20944/preprints202402.1566.v2
Hussain, A.; Aslam, A.; Tripura, S.; Dhanawat, V.; Shinde, V. Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis. Preprints2024, 2024021566. https://doi.org/10.20944/preprints202402.1566.v2
APA Style
Hussain, A., Aslam, A., Tripura, S., Dhanawat, V., & Shinde, V. (2024). Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis. Preprints. https://doi.org/10.20944/preprints202402.1566.v2
Chicago/Turabian Style
Hussain, A., VIneet Dhanawat and Varun Shinde. 2024 "Weather Forecasting Using Machine Learning Techniques: Rainfall and Temperature Analysis" Preprints. https://doi.org/10.20944/preprints202402.1566.v2
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
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
