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
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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
AMA 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
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