Paper
19 July 2024 Surface settlement monitoring in green mining areas based on PSO-BP neural network algorithm
Junwen Zhang, Bao Wang
Author Affiliations +
Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131817S (2024) https://doi.org/10.1117/12.3031165
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China
Abstract
A neural network algorithm based on Small Baseline Subsets Interferometric Synthetic Aperture Radar (SBAS-InSAR) and Particle Swarm Optimization-Back Propagation (PSO-BP) is proposed. Aperture Radar (SBAS-InSAR) and Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithms are proposed as a model for surface settlement monitoring in mining areas. Firstly, the SBAS-InSAR technology is used to obtain the monitoring values of mine surface settlement; then, the PSO-BP prediction model is constructed from a multi-factor perspective by selecting the influencing factors of mine surface settlement and the obtained settlement monitoring values; finally, the validity and reasonableness of this method are analyzed. The experimental results show that SBAS-InSAR can effectively monitor the long-time subsidence of the mine surface, and with the increase of training samples, the residual difference between the PSO-BP prediction value and the SBAS-InSAR subsidence value gradually decreases, and the algorithm convergence iteration speeds up, and the mean-square error decreases. Comparison with the existing monitoring methods and prediction models proves the advantages of SBAS-InSAR in the monitoring of long-time surface subsidence in mining areas.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Junwen Zhang and Bao Wang "Surface settlement monitoring in green mining areas based on PSO-BP neural network algorithm", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131817S (19 July 2024); https://doi.org/10.1117/12.3031165
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KEYWORDS
Mining

Evolutionary algorithms

Neural networks

Environmental monitoring

Data modeling

Particle swarm optimization

Deformation

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