Temperature, as a critical element of weather forecasting, has consistently attracted extensive public attention. Accurate daily temperature prediction is essential for mitigating economic losses, preventing casualties, and maintaining public safety. However, traditional temperature prediction methods often fail to forecast the temperature promptly and effectively. To achieve more accurate daily temperatures prediction, researchers have turned to the recent advancement of artificial intelligence. This study aims to address the prediction of daily temperature in Algiers, by developing a stacking-based ensemble model. Firstly, the data normalization method is employed to preprocess the raw temperature data of Algiers in the experiment. Secondly, Decision Tree, K-Nearest Neighbors, Linear Regression, Random Forest, Recurrent Neural Network, and Support Vector Regression are selected as base models to predict the daily temperature. Finally, a stacking-based ensemble model with Recurrent Neural Network as the meta regressor (S-RNN) is applied for further accurate prediction. The experiment involves evaluating multiple metrics on the dataset to assess the performance of the model in predicting daily temperatures in Algiers. The experimental results indicate that the ensemble model outperforms other base models in addressing the challenges of daily temperature prediction. Meanwhile, this study confirms the significant potential in the application of stacking-based ensemble learning in the field of daily temperature prediction.
| Published in | American Journal of Environmental Science and Engineering (Volume 8, Issue 3) |
| DOI | 10.11648/j.ajese.20240803.13 |
| Page(s) | 79-85 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Ensemble Model, Stacking, Daily Temperature, Prediction
| [1] | Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., and Younis, I. A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research. 2022, 29, 42539-42559. |
| [2] | An, H. Y., Li, Q. L., Lv, X. Y., Li, G. X., Qian, Q. F., Zhou, G. B., Nie, G. Z., Zhang, L. J., and Zhu, L. W. Forecasting daily extreme temperatures in Chinese representative cities using artificial intelligence models. Weather and Climate Extremes. 2023, 42, 100621. |
| [3] | Bauer, P., Thorpe, A., and Brunet, G. The quiet revolution of numerical weather prediction. Nature. 2015, 525(7567), 47-55. |
| [4] | Glahn, H. R., and Lowry, D. A. The use of Model Output Statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology. 1972, 11(8), 1203-1211. |
| [5] | Glahn, B. Determining an optimal decay factor for Bias-Correcting MOS temperature and Dewpoint forecasts. Weather and Forecasting. 2014, 29(4), 1076-1090. |
| [6] | Abdel-Aal, R. E., and Elhadidy, M. A. Modeling and forecasting the daily maximum temperature using abductive machine learning. Weather and Forecasting. 1995, 10(2), 310-325. |
| [7] | Paniagua-Tineo, A., Salcedo-Sanz, S., Casanova-Mateo, C., Ortiz-García, E., Cony, M., and Hernández-Martín, E. Prediction of daily maximum temperature using a support vector regression algorithm. Renewable Energy. 2011, 36(11), 3054-3060. |
| [8] | Krenn, M., Buffoni, L., Coutinho, B., Eppel, S., Foster, J. G., Gritsevskiy, A., Lee, H., Lu, Y., Moutinho, J. P., Sanjabi, N., Sonthalia, R., Tran, N. M., Valente, F., Xie, Y., Yu, R., and Kopp, M. Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network. Nature Machine Intelligence. 2023, 5(11), 1326-1335. |
| [9] | Kumar, V., Aydav, P. S. S., and Minz, S. Multi-view ensemble learning using multi-objective particle swarm optimization for high dimensional data classification. Journal of King Saud University. Computer and Information Sciences. 2022, 34(10), 8523-8537. |
| [10] | Mienye, I. D., and Sun, Y. A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access. 2022, 10, 99129-99149. |
| [11] | Mohammed, A., and Kora, R. A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University. Computer and Information Sciences. 2023, 35(2), 757-774. |
| [12] | Chatzimparmpas, A., Martins, R. M., Kucher, K., and Kerren, A. StackGenVIS: alignment of data, algorithms, and models for stacking ensemble learning using performance metrics. IEEE Transactions on Visualization and Computer Graphics. 2021, 27(2), 1547-1557. |
| [13] | Hajihosseinlou, M., Maghsoudi, A., and Ghezelbash, R. Stacking: A novel data-driven ensemble machine learning strategy for prediction and mapping of Pb-Zn prospectivity in Varcheh district, west Iran. Expert Systems with Applications. 2024, 237, 121668. |
| [14] | He, X., Ghasemian, A., Lee, E., Clauset, A., and Mucha, P. J. Sequential stacking link prediction algorithms for temporal networks. Nature Communications. 2024, 15, 1364. |
| [15] | Cui, S., Yin, Y. Q., Wang, D. J., Li, Z. W., and Wang, Y. Z. A stacking-based ensemble learning method for earthquake casualty prediction. Applied Soft Computing. 2021, 101, 107038. |
| [16] | Jose, D. M., Vincent, A. M., and Dwarakish, G. S. Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques. Scientific Reports. 2022, 12, 4678. |
| [17] | Li, X. Y., Li, Z., Huang, W., and Zhou, P. X. Performance of statistical and machine learning ensembles for daily temperature downscaling. Theoretical and Applied Climatology. 2020, 140(1-2), 571-588. |
| [18] | Bihlo, A. A generative adversarial network approach to (ensemble) weather prediction. Neural Networks.2021, 139, 1-16. |
| [19] | Singh, D., and Singh, B. Investigating the impact of data normalization on classification performance. Applied Soft Computing. 2020, 97, 105524. |
| [20] | Mahmud, M. S., Huang, J. Z., Salloum, S., Emara, T. Z., and Sadatdiynov, K. A survey of data partitioning and sampling methods to support big data analysis. Big Data Mining and Analytics. 2020, 3(2), 85-101. |
APA Style
Xu, Q., Guo, A., Yu, W., He, C. (2024). Evaluating the Performance of a Stacking-Based Ensemble Model for Daily Temperature Prediction. American Journal of Environmental Science and Engineering, 8(3), 79-85. https://doi.org/10.11648/j.ajese.20240803.13
ACS Style
Xu, Q.; Guo, A.; Yu, W.; He, C. Evaluating the Performance of a Stacking-Based Ensemble Model for Daily Temperature Prediction. Am. J. Environ. Sci. Eng. 2024, 8(3), 79-85. doi: 10.11648/j.ajese.20240803.13
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Download@article{10.11648/j.ajese.20240803.13,
author = {Qiwei Xu and Anqi Guo and Wangzhi Yu and Chenfei He},
title = {Evaluating the Performance of a Stacking-Based Ensemble Model for Daily Temperature Prediction
},
journal = {American Journal of Environmental Science and Engineering},
volume = {8},
number = {3},
pages = {79-85},
doi = {10.11648/j.ajese.20240803.13},
url = {https://doi.org/10.11648/j.ajese.20240803.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20240803.13},
abstract = {Temperature, as a critical element of weather forecasting, has consistently attracted extensive public attention. Accurate daily temperature prediction is essential for mitigating economic losses, preventing casualties, and maintaining public safety. However, traditional temperature prediction methods often fail to forecast the temperature promptly and effectively. To achieve more accurate daily temperatures prediction, researchers have turned to the recent advancement of artificial intelligence. This study aims to address the prediction of daily temperature in Algiers, by developing a stacking-based ensemble model. Firstly, the data normalization method is employed to preprocess the raw temperature data of Algiers in the experiment. Secondly, Decision Tree, K-Nearest Neighbors, Linear Regression, Random Forest, Recurrent Neural Network, and Support Vector Regression are selected as base models to predict the daily temperature. Finally, a stacking-based ensemble model with Recurrent Neural Network as the meta regressor (S-RNN) is applied for further accurate prediction. The experiment involves evaluating multiple metrics on the dataset to assess the performance of the model in predicting daily temperatures in Algiers. The experimental results indicate that the ensemble model outperforms other base models in addressing the challenges of daily temperature prediction. Meanwhile, this study confirms the significant potential in the application of stacking-based ensemble learning in the field of daily temperature prediction.
},
year = {2024}
}
TY - JOUR T1 - Evaluating the Performance of a Stacking-Based Ensemble Model for Daily Temperature Prediction AU - Qiwei Xu AU - Anqi Guo AU - Wangzhi Yu AU - Chenfei He Y1 - 2024/09/29 PY - 2024 N1 - https://doi.org/10.11648/j.ajese.20240803.13 DO - 10.11648/j.ajese.20240803.13 T2 - American Journal of Environmental Science and Engineering JF - American Journal of Environmental Science and Engineering JO - American Journal of Environmental Science and Engineering SP - 79 EP - 85 PB - Science Publishing Group SN - 2578-7993 UR - https://doi.org/10.11648/j.ajese.20240803.13 AB - Temperature, as a critical element of weather forecasting, has consistently attracted extensive public attention. Accurate daily temperature prediction is essential for mitigating economic losses, preventing casualties, and maintaining public safety. However, traditional temperature prediction methods often fail to forecast the temperature promptly and effectively. To achieve more accurate daily temperatures prediction, researchers have turned to the recent advancement of artificial intelligence. This study aims to address the prediction of daily temperature in Algiers, by developing a stacking-based ensemble model. Firstly, the data normalization method is employed to preprocess the raw temperature data of Algiers in the experiment. Secondly, Decision Tree, K-Nearest Neighbors, Linear Regression, Random Forest, Recurrent Neural Network, and Support Vector Regression are selected as base models to predict the daily temperature. Finally, a stacking-based ensemble model with Recurrent Neural Network as the meta regressor (S-RNN) is applied for further accurate prediction. The experiment involves evaluating multiple metrics on the dataset to assess the performance of the model in predicting daily temperatures in Algiers. The experimental results indicate that the ensemble model outperforms other base models in addressing the challenges of daily temperature prediction. Meanwhile, this study confirms the significant potential in the application of stacking-based ensemble learning in the field of daily temperature prediction. VL - 8 IS - 3 ER -
Nottingham University Business School China, University of Nottingham Ningbo China, Ningbo, China
Kelley School of Business, Indiana University Bloomington, Bloomington, United States
School of Information Technology & Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
School of Economics & Finance, Zhejiang University of Finance and Economics, Hangzhou, China
