MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect
The construction of a reasonable and reliable deformation prediction model is of great practical significance for dam safety assessment and risk decision-making. Traditional dam deformation prediction models are susceptible to interference from redundant features, weak generalization ability, and a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/14/8538 |
| _version_ | 1797590411182604288 |
|---|---|
| author | Hepeng Liu Denghua Li Yong Ding |
| author_facet | Hepeng Liu Denghua Li Yong Ding |
| author_sort | Hepeng Liu |
| collection | DOAJ |
| description | The construction of a reasonable and reliable deformation prediction model is of great practical significance for dam safety assessment and risk decision-making. Traditional dam deformation prediction models are susceptible to interference from redundant features, weak generalization ability, and a lack of model interpretation. Based on this, a deformation prediction model that considers the lag effect of environmental quantities is proposed. The model first constructs a new deformation lag influence factor based on the plain HST model through the lag quantization algorithm. Secondly, the attention and memory capacity of the model is improved by introducing a multi-head attention mechanism to the features of the long-time domain deformation influence factor, and finally, the extracted dynamic features are transferred to the ConvLSTM model for learning, training, and prediction. The results of the simulation tests based on the measured deformation data of an active dam show that the introduction of the deformation lag factor not only improves the interpretation of the prediction model for deformation but also makes the prediction of deformation more accurate, and it can improve the evaluation indexes such as RMSE by 50%, the nMAPE by 40%, and R<sup>2</sup> by 10% compared with the traditional prediction model. The combined prediction model is more capable of mining the hidden features of the data and has a deeper picture of the overall peak and local extremes of the deformation data, which provides a new way of thinking for the dam deformation prediction model. |
| first_indexed | 2024-03-11T01:20:07Z |
| format | Article |
| id | doaj.art-e88817be834f4b6297d3dbbf9da45411 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:20:07Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e88817be834f4b6297d3dbbf9da454112023-11-18T18:14:30ZengMDPI AGApplied Sciences2076-34172023-07-011314853810.3390/app13148538MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag EffectHepeng Liu0Denghua Li1Yong Ding2School of Science, Nanjing University of Science and Technology, Nanjing 210094, ChinaNanjing Hydraulic Research Institute, Nanjing 210029, ChinaSchool of Science, Nanjing University of Science and Technology, Nanjing 210094, ChinaThe construction of a reasonable and reliable deformation prediction model is of great practical significance for dam safety assessment and risk decision-making. Traditional dam deformation prediction models are susceptible to interference from redundant features, weak generalization ability, and a lack of model interpretation. Based on this, a deformation prediction model that considers the lag effect of environmental quantities is proposed. The model first constructs a new deformation lag influence factor based on the plain HST model through the lag quantization algorithm. Secondly, the attention and memory capacity of the model is improved by introducing a multi-head attention mechanism to the features of the long-time domain deformation influence factor, and finally, the extracted dynamic features are transferred to the ConvLSTM model for learning, training, and prediction. The results of the simulation tests based on the measured deformation data of an active dam show that the introduction of the deformation lag factor not only improves the interpretation of the prediction model for deformation but also makes the prediction of deformation more accurate, and it can improve the evaluation indexes such as RMSE by 50%, the nMAPE by 40%, and R<sup>2</sup> by 10% compared with the traditional prediction model. The combined prediction model is more capable of mining the hidden features of the data and has a deeper picture of the overall peak and local extremes of the deformation data, which provides a new way of thinking for the dam deformation prediction model.https://www.mdpi.com/2076-3417/13/14/8538hysteresisConvLSTMattention mechanismprediction modeldam deformation |
| spellingShingle | Hepeng Liu Denghua Li Yong Ding MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect Applied Sciences hysteresis ConvLSTM attention mechanism prediction model dam deformation |
| title | MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect |
| title_full | MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect |
| title_fullStr | MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect |
| title_full_unstemmed | MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect |
| title_short | MHA-ConvLSTM Dam Deformation Prediction Model Considering Environmental Volume Lag Effect |
| title_sort | mha convlstm dam deformation prediction model considering environmental volume lag effect |
| topic | hysteresis ConvLSTM attention mechanism prediction model dam deformation |
| url | https://www.mdpi.com/2076-3417/13/14/8538 |
| work_keys_str_mv | AT hepengliu mhaconvlstmdamdeformationpredictionmodelconsideringenvironmentalvolumelageffect AT denghuali mhaconvlstmdamdeformationpredictionmodelconsideringenvironmentalvolumelageffect AT yongding mhaconvlstmdamdeformationpredictionmodelconsideringenvironmentalvolumelageffect |