Land subsidence prediction model based on its influencing factors and machine learning methods

Fengkai Li, Guolin Liu (), Qiuxiang Tao and Min Zhai
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Fengkai Li: Shandong University of Science and Technology
Guolin Liu: Shandong University of Science and Technology
Qiuxiang Tao: Shandong University of Science and Technology
Min Zhai: Shandong University of Science and Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 116, issue 3, No 10, 3015-3041

Abstract: Abstract Land subsidence has caused huge economic losses in the Beijing plains (BP) since 1980s. Building land subsidence prediction models that can predict the development of land subsidence is of great significance for improving the safety of cities and reducing economic losses in Eastern Beijing plains. The pattern of evolution of land subsidence is affected by many factors including groundwater level in different aquifers, thicknesses of compressible layers, and static and dynamic loads caused by urban construction. First, we used the small baseline subset Interferometric Synthetic Aperture Radar (SBAS‐InSAR) technology on 47 ENVISAT ASAR images and 48 RADARSAT‐2 images and used Persistent Scatterers Interferometric Aperture Radar (PS-InSAR) technology on 27 Sentinel-1 images to obtain the land subsidence monitoring results from June 2003 to September 2018. Second, the accuracy of the InSAR monitoring results was validated by using leveling benchmark land subsidence monitoring results. Finally, we built land subsidence rate prediction models and land subsidence gradient prediction models by combining land subsidence influencing factors and four machine learning methods including support vector machine (SVM), Gradient Boosting Decision Tree (GBDT), Random forest (RF) and Extremely Randomized Trees (ERT). The findings show: (1) The InSAR monitoring results revealed that the Beijing Plain has experienced serious land subsidence during the 2003–2010, 2011–2015 and 2016–2018 periods. (2): The InSAR monitoring results agreed well with the leveling benchmark monitoring results with the Pearson correlation coefficients of two monitoring results were all greater than 0.95 during the 2003–2010, 2011–2015 and 2016–2018 periods, respectively. (3): We found that the land subsidence prediction based on ERT method is the optimal model among four land subsidence prediction models and that the prediction performance of land subsidence prediction model based on ERT method will be greatly improved when apply this prediction model in sub study areas where the land subsidence mechanism is similar owning to the similar hydrogeological parameters.

Keywords: Land subsidence; InSAR; Pearson correlation coefficient; Land subsidence prediction model; Machine learning methods (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05796-9

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