Acta Geodaetica et Cartographica Sinica ›› 2024, Vol. 53 ›› Issue (6): 1113-1127.doi: 10.11947/j.AGCS.2024.20230387
• Smart Surveying and Mapping • Previous Articles Next Articles
Xunqiang GONG1,2(), Hongyu WANG2,3(
), Tieding LU1,2, Wei YOU3
Received:
2023-09-05
Published:
2024-07-22
Contact:
Hongyu WANG
E-mail:xqgong1988@163.com;wanghyvv@foxmail.com
About author:
GONG Xunqiang (1988—), male, PhD, associate professor, majors in intelligent monitoring of major transportation engineering. E-mail: xqgong1988@163.com
Supported by:
CLC Number:
Xunqiang GONG, Hongyu WANG, Tieding LU, Wei YOU. A general progressive decomposition long-term prediction network model for high-speed railway bridge pier settlement[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1113-1127.
Tab.1
Accuracy indexes of various models in low-frequency observation experiments"
模型 | A1 | A2 | B1 | B2 | ||||
---|---|---|---|---|---|---|---|---|
MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE | |
SVR | 1.267 | 1.302 | 1.365 | 1.414 | 1.126 | 1.196 | 1.327 | 1.353 |
LSTM | 1.185 | 1.212 | 1.240 | 1.550 | 1.191 | 1.229 | 1.361 | 1.382 |
CNN-LSTM | 1.213 | 1.247 | 1.155 | 1.188 | 1.203 | 1.248 | 1.148 | 1.171 |
CNN-Attention-LSTM | 1.061 | 1.095 | 0.813 | 0.905 | 1.145 | 1.192 | 1.083 | 1.107 |
BiLSTM | 1.002 | 1.037 | 1.092 | 1.119 | 1.552 | 1.576 | 1.422 | 1.439 |
CNN-BiLSTM | 1.481 | 1.513 | 1.021 | 1.083 | 1.045 | 1.091 | 1.262 | 1.282 |
Resnet | 0.597 | 0.696 | 1.245 | 1.307 | 1.031 | 1.096 | 0.555 | 0.678 |
GPDLPnet | 0.229 | 0.267 | 0.355 | 0.432 | 0.377 | 0.467 | 0.391 | 0.464 |
Tab.2
Accuracy indexes of various models in high-frequency observation experiments"
测点 | 模型 | SVR | LSTM | CNN-LSTM | CNN-Attention-LSTM | BiLSTM | CNN-BiLSTM | Resnet | GPDLPnet |
---|---|---|---|---|---|---|---|---|---|
E1 | MAE | 1.144 | 0.936 | 0.892 | 0.772 | 0.632 | 0.806 | 0.836 | 0.273 |
RMSE | 1.244 | 0.955 | 0.933 | 0.830 | 0.690 | 0.849 | 1.037 | 0.309 | |
E2 | MAE | 1.412 | 1.032 | 0.772 | 0.780 | 0.349 | 0.748 | 0.934 | 0.247 |
RMSE | 1.613 | 1.050 | 0.811 | 0.818 | 0.453 | 0.767 | 1.092 | 0.355 | |
F1 | MAE | 0.989 | 0.603 | 0.671 | 0.683 | 0.504 | 0.574 | 0.589 | 0.278 |
RMSE | 1.288 | 0.687 | 0.816 | 0.809 | 0.571 | 0.678 | 0.774 | 0.342 | |
F2 | MAE | 0.996 | 0.987 | 0.932 | 0.948 | 0.939 | 0.648 | 0.641 | 0.259 |
RMSE | 1.163 | 1.154 | 1.061 | 1.106 | 1.099 | 0.768 | 0.764 | 0.335 | |
G1 | MAE | 0.459 | 0.675 | 0.408 | 0.337 | 0.381 | 0.350 | 0.437 | 0.157 |
RMSE | 0.571 | 0.746 | 0.471 | 0.433 | 0.457 | 0.439 | 0.506 | 0.242 | |
G2 | MAE | 0.615 | 0.482 | 0.353 | 0.271 | 0.774 | 0.345 | 0.673 | 0.233 |
RMSE | 0.692 | 0.538 | 0.395 | 0.304 | 0.873 | 0.372 | 0.821 | 0.263 | |
H1 | MAE | 0.785 | 1.117 | 1.175 | 0.938 | 1.163 | 1.023 | 0.639 | 0.423 |
RMSE | 0.953 | 1.205 | 1.278 | 1.008 | 1.243 | 1.106 | 0.823 | 0.533 | |
H2 | MAE | 0.663 | 0.803 | 0.855 | 0.902 | 0.907 | 0.875 | 0.315 | 0.204 |
RMSE | 0.736 | 0.918 | 1.001 | 1.049 | 1.025 | 1.009 | 0.401 | 0.243 |
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