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.; Tripura, S.; Aslam, A. Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh. Preprints2024, 2024021566. https://doi.org/10.20944/preprints202402.1566.v1
Hussain, A.; Tripura, S.; Aslam, A. Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh. Preprints 2024, 2024021566. https://doi.org/10.20944/preprints202402.1566.v1
Hussain, A.; Tripura, S.; Aslam, A. Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh. Preprints2024, 2024021566. https://doi.org/10.20944/preprints202402.1566.v1
APA Style
Hussain, A., Tripura, S., & Aslam, A. (2024). Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh. Preprints. https://doi.org/10.20944/preprints202402.1566.v1
Chicago/Turabian Style
Hussain, A., Sajib Tripura and Ayesha Aslam. 2024 "Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh" Preprints. https://doi.org/10.20944/preprints202402.1566.v1
Abstract
Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.
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.