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• W4362471566 endingPage "112778" @default.
• W4362471566 startingPage "112778" @default.
• W4362471566 abstract "Urban ground deformation has been widely concerned because of its safety risks and impact on the normal construction of cities. To better characterize the ground deformation, this research develops a hybrid approach that integrates multiscale geographically weighted regression (MGWR) and satellite monitoring to quantitively estimate the impact of driving force factors on urban ground deformation. The small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method is processed to monitor the urban ground deformation with the help of satellite images. The MGWR model is developed to explore the influencing factors of ground deformation in different geographical locations. Regression analysis is performed to explore the influence of driving factors on ground deformation and deformation velocity. A realistic case in Singapore is investigated to demonstrate the MGWR model and its superiority. The results indicate that: (1) With the help of satellite monitoring images, the MGWR model has excellent fitting performance with a R2 value of 0.747 in predicting ground deformation velocity and that of 0.722 in predicting ground deformation; (2) Water and build are two main driving force factors toward ground deformation velocity, while build, population, and elevation are three main driving force factors on monthly ground deformation; (3) The goodness-of-fit of MGWR model outperforms GWR and OLS models in validation metrics. This MGWR approach provides insights into a better understanding of driving force factors in urban ground deformation, aiding the proactive control plans for mitigating the surface subsidence disaster." @default.
• W4362471566 created "2023-04-05" @default.
• W4362471566 creator A5031856421 @default.
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• W4362471566 date "2023-06-01" @default.
• W4362471566 modified "2023-10-18" @default.
• W4362471566 title "Driving forces analysis of urban ground deformation using satellite monitoring and multiscale geographically weighted regression" @default.
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• W4362471566 doi "https://doi.org/10.1016/j.measurement.2023.112778" @default.
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