A Multifactor-Based Random Forest Regression Model to Reconstruct a Continuous Deformation Map in Xi'an, China

The synthetic aperture radar interferometry (InSAR) technique is an effective means to monitor ground deformation with high spatial resolution over large areas. However, it is still difficult to obtain the spatially continuous deformation map due to SAR decorrelation or SAR distortion, which greatly limits the usage of the InSAR deformation map, especially for spatiotemporal characterizing and mechanism inversion. Some conventional methods (e.g., spatial interpolation) rely only on the deformation measurements without considering the influence factors, leading to the inaccuracy of the deformation prediction. So, we propose a multifactor-based machine learning model, namely the K-RFR model, that combines K-means clustering and random forest regression algorithm to reconstruct a continuous deformation map, where the influence factors on ground deformation are considered, such as land use, geological engineering, and under groundwater extraction. We take the city of Xi'an, China, as the study area where SBAS-InSAR was used to obtain the ground deformation maps from 2012 to 2015. Fourteen influence factors are employed, including confined water level, change of confined water, phreatic water level, change of phreatic water, rainfall, ground fissures, stratigraphic lithology, landform, hydrogeology, engineering geology, type of land use, soil type, GDP, and DEM, where the K-means clustering method is used to reduce the influence of spatial heterogeneity. The study area is divided into three homogeneous regions and modeled independently, where the mean squared errors of region I-III are 2.9 mm, 2.3 mm, and 3.9 mm, respectively, and the mean absolute errors are 2.5 mm, 1.0 mm, and 2.8 mm, respectively. Finally, the continuous ground deformation maps of Xi'an from 2012 to 2015 are reconstructed. We compared the new method with two interpolation methods. Results show that the correlation coefficient between prediction and InSAR measurements of the new model is 0.94, whereas the ordinary Kriging method is 0.69, and the IDW method is only 0.63. This study provides an effective means to predict the continuous surface deformation over a large area.

Automated summary

Synthetic Aperture Radar Interferometry (InSAR) is an effective technique for monitoring large-scale ground deformation with high spatial resolution. However, it is challenging to obtain a spatially continuous deformation map due to SAR decorrelation or distortion. In this study, we propose a multifactor-based machine learning model called the K-RFR model, which combines K-means clustering and random forest regression algorithms to reconstruct a continuous deformation map. The model takes into account various influence factors on ground deformation, such as land use, geological engineering, and groundwater extraction. The study conducted in Xi'an, China, using the SBAS-InSAR technique, demonstrates the effectiveness of the proposed model in predicting ground deformation. The new model outperforms traditional interpolation methods, achieving a higher correlation coefficient with the InSAR measurements.