Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR
The random forest algorithm and whale optimization algorithm were introduced in the construction of the prediction model of concrete dam deformation based on WOA-RFR to improve the prediction accuracy and model performance. The random forest model belonging to the machine learning algorithm has many...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2024-07-01
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| Series: | Renmin Zhujiang |
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| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.014 |
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| _version_ | 1841530611817250816 |
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| author | FENG Yu WU Yunxing GU Wenjing PANG Qiong GU Yanchang CHEN Siyu |
| author_facet | FENG Yu WU Yunxing GU Wenjing PANG Qiong GU Yanchang CHEN Siyu |
| author_sort | FENG Yu |
| collection | DOAJ |
| description | The random forest algorithm and whale optimization algorithm were introduced in the construction of the prediction model of concrete dam deformation based on WOA-RFR to improve the prediction accuracy and model performance. The random forest model belonging to the machine learning algorithm has many advantages such as strong generalization ability and fast training speed, and it has a strong mapping capability for nonlinear features. However, because different parameters and corresponding parameter combinations of the primitive random forest algorithm have a great influence on the improvement and stability of the model performance, the effectiveness of the results cannot be guaranteed under the manual empirical method. Therefore, to address the parameter calibration of the random forest model, the whale optimization algorithm with strong global search ability is introduced to conduct combination optimization on key parameters. The aim is to further enhance the model's generalization ability and robustness at the same time as obtaining optimal parameter combinations. The monitoring model of dam deformation is built by using the random forest optimized by whale algorithm for an actual project, and the coefficient of determination, root mean square error (RMSE), and mean absolute percentage error (MAPE) are introduced to evaluate and compare the excellent performance of the proposed models. The prediction results were compared with different intelligent optimization algorithms and multiple control models. The results show that the WOA-RFR prediction model has higher prediction accuracy and stability, and WOA optimization significantly improves the model performance. |
| format | Article |
| id | doaj-art-a1092407980d495bb41fb6b557fd9c29 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-07-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-a1092407980d495bb41fb6b557fd9c292025-01-15T03:01:14ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-07-014511812456612141Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFRFENG YuWU YunxingGU WenjingPANG QiongGU YanchangCHEN SiyuThe random forest algorithm and whale optimization algorithm were introduced in the construction of the prediction model of concrete dam deformation based on WOA-RFR to improve the prediction accuracy and model performance. The random forest model belonging to the machine learning algorithm has many advantages such as strong generalization ability and fast training speed, and it has a strong mapping capability for nonlinear features. However, because different parameters and corresponding parameter combinations of the primitive random forest algorithm have a great influence on the improvement and stability of the model performance, the effectiveness of the results cannot be guaranteed under the manual empirical method. Therefore, to address the parameter calibration of the random forest model, the whale optimization algorithm with strong global search ability is introduced to conduct combination optimization on key parameters. The aim is to further enhance the model's generalization ability and robustness at the same time as obtaining optimal parameter combinations. The monitoring model of dam deformation is built by using the random forest optimized by whale algorithm for an actual project, and the coefficient of determination, root mean square error (RMSE), and mean absolute percentage error (MAPE) are introduced to evaluate and compare the excellent performance of the proposed models. The prediction results were compared with different intelligent optimization algorithms and multiple control models. The results show that the WOA-RFR prediction model has higher prediction accuracy and stability, and WOA optimization significantly improves the model performance.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.014concrete damdam deformation predictionrandom forest modelwhale optimization algorithm |
| spellingShingle | FENG Yu WU Yunxing GU Wenjing PANG Qiong GU Yanchang CHEN Siyu Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR Renmin Zhujiang concrete dam dam deformation prediction random forest model whale optimization algorithm |
| title | Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR |
| title_full | Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR |
| title_fullStr | Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR |
| title_full_unstemmed | Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR |
| title_short | Prediction and Monitoring Model of Concrete Dam Deformation Based on WOA-RFR |
| title_sort | prediction and monitoring model of concrete dam deformation based on woa rfr |
| topic | concrete dam dam deformation prediction random forest model whale optimization algorithm |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.07.014 |
| work_keys_str_mv | AT fengyu predictionandmonitoringmodelofconcretedamdeformationbasedonwoarfr AT wuyunxing predictionandmonitoringmodelofconcretedamdeformationbasedonwoarfr AT guwenjing predictionandmonitoringmodelofconcretedamdeformationbasedonwoarfr AT pangqiong predictionandmonitoringmodelofconcretedamdeformationbasedonwoarfr AT guyanchang predictionandmonitoringmodelofconcretedamdeformationbasedonwoarfr AT chensiyu predictionandmonitoringmodelofconcretedamdeformationbasedonwoarfr |