Prediction for the Settlement of Concrete Face Rockfill Dams Using Optimized LSTM Model via Correlated Monitoring Data
Abstract
:1. Introduction
2. The Interpretation Model for the Settlement of CFRD
3. LSTM-CS MMP Prediction Model for the Settlement of CFRD
3.1. Data Clustering Based on K-Means++ Algorithm
3.2. CS Algorithm Optimized LSTM
3.2.1. Long Short-Term Memory Structure
3.2.2. Cuckoo Search Algorithm
4. Project Overview
5. Results and Discussion
5.1. Clustering Results of Monitoring Data Series
5.2. Optimization of Parameters in LSTM
5.3. Fitting and Prediction Results
5.3.1. Selection of the Input Variables in the Model
5.3.2. Fitting and Prediction Performance of M–LSTM Model
5.3.3. Comparison between Different Models
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Fitting and Predicting Results
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Hidden Layers | Hidden Nodes | Learning Rate | |
---|---|---|---|
Upper bound | 10 | 20 | |
Lower bound | 1 | 1 | |
Optimized parameters | 2 | 7 |
Monitoring Point | M–LSTM | LSTM | BPNN | HST |
---|---|---|---|---|
LD1-2 | 0.843 | 0.985 | 0.754 | 0.576 |
LD2-2 | 0.987 | 0.807 | 0.971 | 0.918 |
LD2-3 | 0.907 | 0.989 | 0.873 | 0.665 |
LD3-2 | 0.995 | 0.926 | 0.991 | 0.951 |
LD3-3 | 0.991 | 0.93 | 0.905 | 0.937 |
LD3-4 | 0.919 | 0.541 | 0.829 | 0.745 |
LD3-5 | 0.799 | 0.991 | 0.753 | 0.409 |
LD4-2 | 0.995 | 0.983 | 0.994 | 0.962 |
LD4-3 | 0.994 | 0.991 | 0.99 | 0.96 |
LD5-2 | 0.995 | 0.985 | 0.987 | 0.961 |
LD5-3 | 0.993 | 0.95 | 0.761 | 0.946 |
LD5-4 | 0.974 | 0.781 | 0.947 | 0.87 |
LD5-5 | 0.88 | 0.99 | 0.867 | 0.635 |
LD6-2 | 0.993 | 0.974 | 0.988 | 0.941 |
LD6-3 | 0.981 | 0.779 | 0.917 | 0.865 |
LD6-4 | 0.87 | 0.754 | 0.793 | 0.579 |
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Hu, Y.; Gu, C.; Meng, Z.; Shao, C.; Min, Z. Prediction for the Settlement of Concrete Face Rockfill Dams Using Optimized LSTM Model via Correlated Monitoring Data. Water 2022, 14, 2157. https://doi.org/10.3390/w14142157
Hu Y, Gu C, Meng Z, Shao C, Min Z. Prediction for the Settlement of Concrete Face Rockfill Dams Using Optimized LSTM Model via Correlated Monitoring Data. Water. 2022; 14(14):2157. https://doi.org/10.3390/w14142157
Chicago/Turabian StyleHu, Yating, Chongshi Gu, Zhenzhu Meng, Chenfei Shao, and Zhongze Min. 2022. "Prediction for the Settlement of Concrete Face Rockfill Dams Using Optimized LSTM Model via Correlated Monitoring Data" Water 14, no. 14: 2157. https://doi.org/10.3390/w14142157
APA StyleHu, Y., Gu, C., Meng, Z., Shao, C., & Min, Z. (2022). Prediction for the Settlement of Concrete Face Rockfill Dams Using Optimized LSTM Model via Correlated Monitoring Data. Water, 14(14), 2157. https://doi.org/10.3390/w14142157