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A Hybrid Feature Selection-multidimensional LSTM Framework for Deformation Prediction of Super High Arch Dams
Developing a deformation prediction model with strong robustness and applicability based on monitoring data is an important task in establishing a safety monitoring system for super high arch dams. This study proposes a hybrid deep learning (DL) model for deformation prediction, named HFS/xs-MLSTM. The proposed multidimensional long short-term memory (MLSTM) DL achieves integrated modeling of two factors, environment and temporal correlation, by constructing a multivariate matrix, and achieves sequential prediction by rolling through the sliding windows. This operation solves the problem of insufficient prediction accuracy due to the traditional method considering only a single factor. To address the problems of computational time consumption and DL overfitting caused by variable redundancy, a hybrid feature selection method (HFS/xs) based on eXtreme gradient boosting and Spearman is proposed. Analysis shows that HFS/xs can comprehensively reflect the intrinsic laws of the data and effectively filter out the most concise set of variables, which greatly improves the operability and stability of MLSTM. Compared with traditional methods, HFS/xs-MLSTM has the best performance and applicability for deformation prediction in different zones and operating conditions. This study can provide reliable a priori knowledge for the construction of safety monitoring system for super high arch dams.
A Hybrid Feature Selection-multidimensional LSTM Framework for Deformation Prediction of Super High Arch Dams
Developing a deformation prediction model with strong robustness and applicability based on monitoring data is an important task in establishing a safety monitoring system for super high arch dams. This study proposes a hybrid deep learning (DL) model for deformation prediction, named HFS/xs-MLSTM. The proposed multidimensional long short-term memory (MLSTM) DL achieves integrated modeling of two factors, environment and temporal correlation, by constructing a multivariate matrix, and achieves sequential prediction by rolling through the sliding windows. This operation solves the problem of insufficient prediction accuracy due to the traditional method considering only a single factor. To address the problems of computational time consumption and DL overfitting caused by variable redundancy, a hybrid feature selection method (HFS/xs) based on eXtreme gradient boosting and Spearman is proposed. Analysis shows that HFS/xs can comprehensively reflect the intrinsic laws of the data and effectively filter out the most concise set of variables, which greatly improves the operability and stability of MLSTM. Compared with traditional methods, HFS/xs-MLSTM has the best performance and applicability for deformation prediction in different zones and operating conditions. This study can provide reliable a priori knowledge for the construction of safety monitoring system for super high arch dams.
A Hybrid Feature Selection-multidimensional LSTM Framework for Deformation Prediction of Super High Arch Dams
KSCE J Civ Eng
Cao, Enhua (author) / Bao, Tengfei (author) / Li, Hui (author) / Xie, Xiang (author) / Yuan, Rongyao (author) / Hu, Shaopei (author) / Wang, Wenjun (author)
KSCE Journal of Civil Engineering ; 26 ; 4603-4616
2022-11-01
14 pages
Article (Journal)
Electronic Resource
English
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