MOUNTAINOUS AREA SLOPE DISPLACEMENT PREDICTION METHOD BASED ON MI-GRA AND IMPROVED PSO-LSTM
Disclosed in the present invention is a mountainous area slope displacement prediction method based on MI-GRA and an improved PSO-LSTM. The method comprises the following steps: (1) collecting and constructing original data for slope displacement prediction; (2) on the basis of the constructed origi...
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| creator | WANG, Wubin LI, Yandong DENG, Zhixing DONG, Minqi XIE, Kang |
| description | Disclosed in the present invention is a mountainous area slope displacement prediction method based on MI-GRA and an improved PSO-LSTM. The method comprises the following steps: (1) collecting and constructing original data for slope displacement prediction; (2) on the basis of the constructed original data for slope displacement prediction, establishing an MI-GRA-based slope displacement feature selection model; (3) using data subjected to feature selection as an optimal feature set input for slope displacement prediction, and establishing an improved-PSO-LSTM-based slope displacement prediction model; and (4) performing model prediction and testing on the established slope prediction model. The present method solves the problems of: a previous prediction algorithm itself having a static characteristic and being unable to consider historical information of slope displacement, thereby restricting the improvement of the prediction precision; previous slope displacement prediction only focusing on the displacem |
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The method comprises the following steps: (1) collecting and constructing original data for slope displacement prediction; (2) on the basis of the constructed original data for slope displacement prediction, establishing an MI-GRA-based slope displacement feature selection model; (3) using data subjected to feature selection as an optimal feature set input for slope displacement prediction, and establishing an improved-PSO-LSTM-based slope displacement prediction model; and (4) performing model prediction and testing on the established slope prediction model. 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The method comprises the following steps: (1) collecting and constructing original data for slope displacement prediction; (2) on the basis of the constructed original data for slope displacement prediction, establishing an MI-GRA-based slope displacement feature selection model; (3) using data subjected to feature selection as an optimal feature set input for slope displacement prediction, and establishing an improved-PSO-LSTM-based slope displacement prediction model; and (4) performing model prediction and testing on the established slope prediction model. 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The method comprises the following steps: (1) collecting and constructing original data for slope displacement prediction; (2) on the basis of the constructed original data for slope displacement prediction, establishing an MI-GRA-based slope displacement feature selection model; (3) using data subjected to feature selection as an optimal feature set input for slope displacement prediction, and establishing an improved-PSO-LSTM-based slope displacement prediction model; and (4) performing model prediction and testing on the established slope prediction model. The present method solves the problems of: a previous prediction algorithm itself having a static characteristic and being unable to consider historical information of slope displacement, thereby restricting the improvement of the prediction precision; previous slope displacement prediction only focusing on the displacem</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | MOUNTAINOUS AREA SLOPE DISPLACEMENT PREDICTION METHOD BASED ON MI-GRA AND IMPROVED PSO-LSTM |
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