Prediction of subsidence in Ruzhou City based on random forest and SBAS-InSAR

Baojing Zhang; Zhiyong Wang; Shichang Sun

doi:10.1117/12.3010361

15 November 2023 Prediction of subsidence in Ruzhou City based on random forest and SBAS-InSAR

Baojing Zhang, Zhiyong Wang, Shichang Sun

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 1281527 (2023) https://doi.org/10.1117/12.3010361
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

Aiming at the problem that the low coherence point subsidence value cannot be accurately obtained in the monitoring process of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), a spatial subsidence prediction method in mining area combining random forest and SBAS-InSAR was proposed. Based on 17 Sentinel-1A images from 2020 to 2021, the land surface subsidence values of 825 high coherence points in Chaochuan Mine in Ruzhou City were obtained by SBAS-InSAR.785 points were randomly selected and input into the random forest model to predict the remaining 40 points. The prediction results were compared with the SBAS-InSAR monitoring results. The results showed that the average relative error and R² between them are 1.67% and 0.93, and the results are accurate. This method can be applied to the subsidence prediction of low coherence points.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Baojing Zhang, Zhiyong Wang, and Shichang Sun "Prediction of subsidence in Ruzhou City based on random forest and SBAS-InSAR", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 1281527 (15 November 2023); https://doi.org/10.1117/12.3010361

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