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Article

Monitoring and Prediction of Ground Surface Settlement in Kunming Urban Area by Building GWO-LSTM Model Based on TS-InSAR

1
School of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China
2
Yunnan Field Scientific Observation and Research Station of Land Use in Luliang Mountain Basin, Kunming 650201, China
3
Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand
4
Yunnan Institute of Geology and Mineral Surveying Co., Ltd., Kunming 650218, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2024, 14(14), 6036; https://doi.org/10.3390/app14146036
Submission received: 6 May 2024 / Revised: 7 July 2024 / Accepted: 8 July 2024 / Published: 10 July 2024
(This article belongs to the Special Issue Intelligent Computing and Remote Sensing—2nd Edition)

Abstract

Abstract: Long-term series monitoring of ground surface settlement by remote sensing has become an effective method. Based on TS-InSAR (time series Interferometric Synthetic Aperture Radar) interferometry, this paper proposes a new model based on the Grey Wolf Optimizer (GWO) and Long Short-Term Memory (LSTM) to monitor and predict the ground surface settlement in Kunming City. The results show that the MAPE (mean absolute percentage error), RMSE (root mean square error), and MAE (mean absolute error) of GWO-LSTM are significantly reduced. R2 (goodness of fit) in the six sub-study areas of Kunming City is improved in comparison to the LSTM model. The problem of manual parameter selection in the LSTM model is solved by the GWO algorithm to select parameters automatically. This approach not only significantly reduces the model’s training time but also identifies the most suitable network parameters. This can bring the best performance. Based on TS-InSAR data, the prediction of urban ground surface settlement by the GWO-LSTM model has good accuracy and robustness, which offers a scientific foundation for monitoring and issuing early warnings about urban land disasters.
Keywords: GWO-LSTM; TS-InSAR; ground surface settlement; Kunming GWO-LSTM; TS-InSAR; ground surface settlement; Kunming

Share and Cite

MDPI and ACS Style

Li, J.; Li, B.; Peng, Y.; Tang, S.; Chen, Y.; Pei, W. Monitoring and Prediction of Ground Surface Settlement in Kunming Urban Area by Building GWO-LSTM Model Based on TS-InSAR. Appl. Sci. 2024, 14, 6036. https://doi.org/10.3390/app14146036

AMA Style

Li J, Li B, Peng Y, Tang S, Chen Y, Pei W. Monitoring and Prediction of Ground Surface Settlement in Kunming Urban Area by Building GWO-LSTM Model Based on TS-InSAR. Applied Sciences. 2024; 14(14):6036. https://doi.org/10.3390/app14146036

Chicago/Turabian Style

Li, Jianhua, Bolin Li, Yilong Peng, Shaofan Tang, Yongzhi Chen, and Wenjuan Pei. 2024. "Monitoring and Prediction of Ground Surface Settlement in Kunming Urban Area by Building GWO-LSTM Model Based on TS-InSAR" Applied Sciences 14, no. 14: 6036. https://doi.org/10.3390/app14146036

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