Ground subsidence risk assessment method using PS-InSAR and LightGBM: a case study of Shanghai metro network

References

• Abdollahi, S., H. R. Pourghasemi, G. A. Ghanbarian, and R. Safaeian. 2019. “Prioritization of Effective Factors in the Occurrence of Land Subsidence and its Susceptibility Mapping Using an SVM Model and Their Different Kernel Functions.” Bulletin of Engineering Geology and the Environment 78 (6): 4017–4034. https://doi.org/10.1007/s10064-018-1403-6.
   Web of Science ®Google Scholar
• An, B., Y. Jiang, C. Wang, P. Shen, T. Song, C. Hu, and K. Liu. 2023. “Ground Infrastructure Monitoring in Coastal Areas Using Time-Series inSAR Technology: The Case Study of Pudong International Airport, Shanghai.” International Journal of Digital Earth 16 (1): 355–374. https://doi.org/10.1080/17538947.2023.2171144.
   Web of Science ®Google Scholar
• Bagheri-Gavkosh, M., S. M. Hosseini, B. Ataie-Ashtiani, Y. Sohani, H. Ebrahimian, F. Morovat, and S. Ashrafi. 2021. “Land Subsidence: A Global Challenge.” Science of The Total Environment 778: 146193. https://doi.org/10.1016/j.scitotenv.2021.146193.
   PubMed Web of Science ®Google Scholar
• Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti. 2002. “A new Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms.” IEEE Transactions on Geoscience and Remote Sensing 40 (11): 2375–2383. https://doi.org/10.1109/TGRS.2002.803792.
   Web of Science ®Google Scholar
• Bianchini, S., L. Solari, M. Del Soldato, F. Raspini, R. Montalti, A. Ciampalini, and N. Casagli. 2019. “Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic.” Remote Sensing 11 (17): 2015. https://doi.org/10.3390/rs11172015.
   Web of Science ®Google Scholar
• Cabral-Cano, E., T. H. Dixon, F. Miralles-Wilhelm, O. Díaz-Molina, O. Sánchez-Zamora, and R. E. Carande. 2008. “Space Geodetic Imaging of Rapid Ground Subsidence in Mexico City.” Geological Society of America Bulletin 120 (11-12): 1556–1566. https://doi.org/10.1130/B26001.1.
   Web of Science ®Google Scholar
• Cao, C., P. Xu, Y. Wang, J. Chen, L. Zheng, and C. Niu. 2016. “Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas.” Sustainability 8 (9): 948. https://doi.org/10.3390/su8090948.
   Web of Science ®Google Scholar
• Chan, Y. K., and V. Koo. 2008. “An Introduction to Synthetic Aperture Radar (SAR).” Progress In Electromagnetics Research B 2: 27–60. https://doi.org/10.2528/PIERB07110101.
   Google Scholar
• Chang, L., R. P. Dollevoet, and R. F. Hanssen. 2018. “Monitoring Line-Infrastructure with Multisensor SAR Interferometry: Products and Performance Assessment Metrics.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (5): 1593–1605. https://doi.org/10.1109/TGRS.2002.803792.
   Web of Science ®Google Scholar
• Chen, B., H. Gong, X. Li, K. Lei, L. Zhu, M. Gao, and C. Zhou. 2017. “Characterization and Causes of Land Subsidence in Beijing, China.” International Journal of Remote Sensing 38 (3): 808–826. https://doi.org/10.1080/01431161.2016.1259674.
   Web of Science ®Google Scholar
• Chen, W., X. Xie, J. Wang, B. Pradhan, H. Hong, D. T. Bui, Z. Duan, and J. Ma. 2017. “A Comparative Study of Logistic Model Tree, Random Forest, and Classification and Regression Tree Models for Spatial Prediction of Landslide Susceptibility.” Catena 151: 147–160. https://doi.org/10.1016/j.catena.2016.11.032.
   Web of Science ®Google Scholar
• Cigna, F., B. Osmanoğlu, E. Cabral-Cano, T. H. Dixon, J. A. Ávila-Olivera, V. H. Garduño-Monroy, C. DeMets, and S. Wdowinski. 2012. “Monitoring Land Subsidence and its Induced Geological Hazard with Synthetic Aperture Radar Interferometry: A Case Study in Morelia, Mexico.” Remote Sensing of Environment 117: 146–161. https://doi.org/10.1016/j.rse.2011.09.005.
   Web of Science ®Google Scholar
• Cigna, F., and D. Tapete. 2021. “Present-day Land Subsidence Rates, Surface Faulting Hazard and Risk in Mexico City with 2014–2020 Sentinel-1 IW InSAR.” Remote Sensing of Environment 253: 112161. https://doi.org/10.1016/j.rse.2020.112161.
   Web of Science ®Google Scholar
• Fadhillah, M. F., A. R. Achmad, and C.-W. Lee. 2020. “Integration of InSAR Time-Series Data and GIS to Assess Land Subsidence Along Subway Lines in the Seoul Metropolitan Area, South Korea.” Remote Sensing 12 (21): 3505. https://doi.org/10.3390/rs12213505.
   Web of Science ®Google Scholar
• Fawcett, T. 2006. “An Introduction to ROC Analysis.” Pattern Recognition Letters 27 (8): 861–874. https://doi.org/10.1016/j.patrec.2005.10.010.
   Web of Science ®Google Scholar
• Ferretti, A., C. Prati, and F. Rocca. 2001. “Permanent Scatterers in SAR Interferometry.” IEEE Transactions on Geoscience and Remote Sensing 39 (1): 8–20. https://doi.org/10.1109/36.898661.
   Web of Science ®Google Scholar
• Friedman, J. H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 29 (5): 1189–1232.
   Web of Science ®Google Scholar
• Gabriel, A. K., R. M. Goldstein, and H. A. Zebker. 1989. “Mapping Small Elevation Changes Over Large Areas: Differential Radar Interferometry.” Journal of Geophysical Research: Solid Earth 94 (B7): 9183–9191. https://doi.org/10.1029/JB094iB07p09183.
   Web of Science ®Google Scholar
• Gong, S., C. Li, and S. Yang. 2009. “The Microscopic Characteristics of Shanghai Soft Clay and its Effect on Soil Body Deformation and Land Subsidence.” Environmental Geology 56 (6): 1051–1056. https://doi.org/10.1007/s00254-008-1205-4.
   Google Scholar
• Graham, L. C. 1974. “Synthetic Interferometer Radar for Topographic Mapping.” Proceedings of the IEEE 62 (6): 763–768. https://doi.org/10.1109/PROC.1974.9516.
   Web of Science ®Google Scholar
• Huang, J., S. Ling, X. Wu, and R. Deng. 2022. “GIS-based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility.” Land 11 (3): 436. https://doi.org/10.3390/land11030436.
   Web of Science ®Google Scholar
• Hussain, M. A., Z. Chen, Y. Zheng, M. Shoaib, J. Ma, I. Ahmad, A. Asghar, and J. Khan. 2022. “PS-InSAR Based Monitoring of Land Subsidence by Groundwater Extraction for Lahore Metropolitan City, Pakistan.” Remote Sensing 14 (16): 3950. https://doi.org/10.3390/rs14163950.
   Web of Science ®Google Scholar
• Jin, B., K. Yin, Q. Li, L. Gui, T. Yang, B. Zhao, B. Guo, T. Zeng, and Z. Ma. 2022. “Susceptibility Analysis of Land Subsidence Along the Transmission Line in the Salt Lake Area Based on Remote Sensing Interpretation.” Remote Sensing 14 (13): 3229. https://doi.org/10.3390/rs14133229.
   Web of Science ®Google Scholar
• Ke, G., Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu. 2017. “Lightgbm: A Highly Efficient Gradient Boosting Decision Tree.” Advances in Neural Information Processing Systems 30), https://doi.org/10.5555/3294996.3295074.
   Google Scholar
• Kim, K., J. Kim, T.-Y. Kwak, and C.-K. Chung. 2018. “Logistic Regression Model for Sinkhole Susceptibility due to Damaged Sewer Pipes.” Natural Hazards 93 (2): 765–785. https://doi.org/10.1007/s11069-018-3323-y.
   Web of Science ®Google Scholar
• Lee, S., and I. Park. 2013. “Application of Decision Tree Model for the Ground Subsidence Hazard Mapping Near Abandoned Underground Coal Mines.” Journal of Environmental Management 127: 166–176. https://doi.org/10.1016/j.jenvman.2013.04.010.
   PubMed Web of Science ®Google Scholar
• Lee, S., I. Park, and J.-K. Choi. 2012. “Spatial Prediction of Ground Subsidence Susceptibility Using an Artificial Neural Network.” Environmental Management 49 (2): 347–358. https://doi.org/10.1007/s00267-011-9766-5.
   PubMed Web of Science ®Google Scholar
• Li, D., B. Li, Y. Zhang, C. Fan, H. Xu, and X. Hou. 2022. “Spatial and Temporal Characteristics Analysis for Land Subsidence in Shanghai Coastal Reclamation Area Using PS-InSAR Method.” Frontiers in Marine Science 9: 1000523. https://doi.org/10.3389/fmars.2022.1000523.
   Web of Science ®Google Scholar
• Li, J., L. Zhou, Z. Zhu, J. Qin, L. Xian, D. Zhang, and L. Huang. 2022. “Surface Deformation Mechanism Analysis in Shanghai Areas Based on TS-InSAR Technology.” Remote Sensing 14 (17): 4368. https://doi.org/10.3390/rs14174368.
   Web of Science ®Google Scholar
• Lu, Z., R. Fang, H. Yao, Z. Hu, and J. Liu. 2018. “Evaluation and Analysis of the Traffic Load–Induced Settlement of Roads on Soft Subsoils with low Embankments.” International Journal of Geomechanics 18 (6): 04018043. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001123.
   Web of Science ®Google Scholar
• Macchiarulo, V., P. Milillo, C. Blenkinsopp, C. Reale, and G. Giardina. 2021. “Multi-temporal InSAR for Transport Infrastructure Monitoring: Recent Trends and Challenges.” Proceedings of the Institution of Civil Engineers-Bridge Engineering 176 (2): 92–117. https://doi.org/10.1680/jbren.21.00039.
   Web of Science ®Google Scholar
• Malik, K., D. Kumar, D. Perissin, and B. Pradhan. 2022. “Estimation of Ground Subsidence of New Delhi, India Using PS-InSAR Technique and Multi-Sensor Radar Data.” Advances in Space Research 69 (4): 1863–1882. https://doi.org/10.1016/j.asr.2021.08.032.
   Web of Science ®Google Scholar
• Manap, M. A., H. Nampak, B. Pradhan, S. Lee, W. N. A. Sulaiman, and M. F. Ramli. 2014. “Application of Probabilistic-Based Frequency Ratio Model in Groundwater Potential Mapping Using Remote Sensing Data and GIS.” Arabian Journal of Geosciences 7 (2): 711–724. https://doi.org/10.1007/s12517-012-0795-z.
   Web of Science ®Google Scholar
• Minderhoud, P., L. Coumou, L. Erban, H. Middelkoop, E. Stouthamer, and E. Addink. 2018. “The Relation Between Land use and Subsidence in the Vietnamese Mekong Delta.” Science of The Total Environment 634: 715–726. https://doi.org/10.1016/j.scitotenv.2018.03.372.
   PubMed Web of Science ®Google Scholar
• Mohammady, M., H. R. Pourghasemi, and M. Amiri. 2019. “Land Subsidence Susceptibility Assessment Using Random Forest Machine Learning Algorithm.” Environmental Earth Sciences 78 (1): 1–12. https://doi.org/10.1007/s12665-019-8518-3.
   Google Scholar
• Motagh, M., Y. Djamour, T. R. Walter, H.-U. Wetzel, J. Zschau, and S. Arabi. 2007. “Land Subsidence in Mashhad Valley, Northeast Iran: Results from InSAR, Levelling and GPS.” Geophysical Journal International 168 (2): 518–526. https://doi.org/10.1111/j.1365-246X.2006.03246.x.
   Web of Science ®Google Scholar
• Peck, B.. 1969. “Deep Excavation and Tunnelling in Soft Ground.” 7th International Conference on Soil Mechanics and Foundation Engineering (Mexico) 4: 225–290.
   Google Scholar
• Reinders, K. J., R. F. Hanssen, F. J. van Leijen, and M. Korff. 2021. “Augmented Satellite InSAR for Assessing Short-Term and Long-Term Surface Deformation due to Shield Tunnelling.” Tunnelling and Underground Space Technology 110: 103745. https://doi.org/10.1016/j.tust.2020.103745.
   Web of Science ®Google Scholar
• Rong, G., S. Alu, K. Li, Y. Su, J. Zhang, Y. Zhang, and T. Li. 2020. “Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China.” Water 12 (11): 3066. https://doi.org/10.3390/w12113066.
   Web of Science ®Google Scholar
• Sachdeva, S., and B. Kumar. 2021. “Comparison of Gradient Boosted Decision Trees and Random Forest for Groundwater Potential Mapping in Dholpur (Rajasthan), India.” Stochastic Environmental Research and Risk Assessment 35 (2): 287–306. https://doi.org/10.1007/s00477-020-01891-0.
   Web of Science ®Google Scholar
• Sato, C., M. Haga, and J. Nishino. 2006. “Land Subsidence and Groundwater Management in Tokyo.” International Review for Environmental Strategies 6 (2): 403–424.
   Google Scholar
• Shen, S., H. Wu, Y. Cui, and Z. Yin. 2014. “Long-term Settlement Behaviour of Metro Tunnels in the Soft Deposits of Shanghai.” Tunnelling and Underground Space Technology 40: 309–323. https://doi.org/10.1016/j.tust.2013.10.013.
   Web of Science ®Google Scholar
• Shen, S., and Y. Xu. 2011. “Numerical Evaluation of Land Subsidence Induced by Groundwater Pumping in Shanghai.” Canadian Geotechnical Journal 48 (9): 1378–1392. https://doi.org/10.1139/t11-049.
   Web of Science ®Google Scholar
• Shi, X., Y. Xue, J. Wu, S. Ye, Y. Zhang, Z. Wei, and J. Yu. 2008. “Characterization of Regional Land Subsidence in Yangtze Delta, China: The Example of Su-Xi-Chang Area and the City of Shanghai.” Hydrogeology Journal 16 (3): 593–607. https://doi.org/10.1007/s10040-007-0237-2.
   Web of Science ®Google Scholar
• Sousa, J. J., A. M. Ruiz, R. F. Hanssen, L. Bastos, A. J. Gil, J. Galindo-Zaldívar, and C. S. de Galdeano. 2010. “PS-InSAR Processing Methodologies in the Detection of Field Surface Deformation—Study of the Granada Basin (Central Betic Cordilleras, Southern Spain).” Journal of Geodynamics 49 (3-4): 181–189. https://doi.org/10.1016/j.jog.2009.12.002.
   Web of Science ®Google Scholar
• Tan, Y., H. Zhu, F. Peng, K. Karlsrud, and B. Wei. 2017. “Characterization of Semi-top-Down Excavation for Subway Station in Shanghai Soft Ground.” Tunnelling and Underground Space Technology 68: 244–261. https://doi.org/10.1016/j.tust.2017.05.028.
   Web of Science ®Google Scholar
• Tien Bui, D., H. Shahabi, A. Shirzadi, K. Chapi, B. Pradhan, W. Chen, K. Khosravi, M. Panahi, B. Bin Ahmad, and L. Saro. 2018. “Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms.” Sensors 18 (8): 2464. https://doi.org/10.3390/s18082464.
   PubMed Web of Science ®Google Scholar
• Tomiyama, N., K. Koike, and M. Omura. 2004. “Detection of Topographic Changes Associated with Volcanic Activities of Mt. Hossho Using D-InSAR.” Advances in Space Research 33 (3): 279–283. https://doi.org/10.1016/S0273-1177(03)00483-6.
   Web of Science ®Google Scholar
• Wang, H., G. Feng, B. Xu, Y. Yu, Z. Li, Y. Du, and J. Zhu. 2017. “Deriving Spatio-Temporal Development of Ground Subsidence due to Subway Construction and Operation in Delta Regions with PS-InSAR Data: A Case Study in Guangzhou, China.” Remote Sensing 9 (10): 1004. https://doi.org/10.3390/rs9101004.
   Web of Science ®Google Scholar
• Wang, R., M. Yang, T. Yang, J. Lin, and M. Liao. 2022. “Decomposing and Mapping Different Scales of Land Subsidence Over Shanghai with X-and C-Band SAR Data Stacks.” International Journal of Digital Earth 15 (1): 478–502. https://doi.org/10.1080/17538947.2022.2036835.
   Web of Science ®Google Scholar
• Wei, Z. 2010. Urban Geological Atlas of Shanghai. Beijing: Geological Publishing House.(in chinese)
   Google Scholar
• Wu, H., S. Shen, and J. Yang. 2017. “Identification of Tunnel Settlement Caused by Land Subsidence in Soft Deposit of Shanghai.” Journal of Performance of Constructed Facilities 31 (6): 04017092. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001082.
   Web of Science ®Google Scholar
• Xie, Z., G. Chen, X. Meng, Y. Zhang, L. Qiao, and L. Tan. 2017. “A Comparative Study of Landslide Susceptibility Mapping Using Weight of Evidence, Logistic Regression and Support Vector Machine and Evaluated by SBAS-InSAR Monitoring: Zhouqu to Wudu Segment in Bailong River Basin, China.” Environmental Earth Sciences 76 (1): 1–19. https://doi.org/10.1007/s12665-017-6640-7.
   Google Scholar
• Xu, S., Y. Chen, and C. Zhao. 2008. “One-dimensional Consolidation Tests of Creep Deformation and Secondary Consolidation Characteristics of Soft Soils in Shanghai Area.” Journal of Engineering Geology 16 (4): 495–501.
   Google Scholar
• Xu, Y., L. Ma, Y. Du, and S. Shen. 2012. “Analysis of Urbanisation-Induced Land Subsidence in Shanghai.” Natural Hazards 63 (2): 1255–1267. https://doi.org/10.1007/s11069-012-0220-7.
   Web of Science ®Google Scholar
• Xu, Y., S. Shen, and Y. Du. 2009. “Geological and Hydrogeological Environment in Shanghai with Geohazards to Construction and Maintenance of Infrastructures.” Engineering Geology 109 (3-4): 241–254. https://doi.org/10.1016/j.enggeo.2009.08.009.
   Web of Science ®Google Scholar
• Xu, Y., S. Shen, D. Ren, and H. Wu. 2016. “Analysis of Factors in Land Subsidence in Shanghai: A View Based on a Strategic Environmental Assessment.” Sustainability 8 (6): 573. https://doi.org/10.3390/su8060573.
   Web of Science ®Google Scholar
• Xue, Y., Y. Zhang, S. Ye, J. Wu, and Q. Li. 2005. “Land Subsidence in China.” Environmental Geology 48 (6): 713–720. https://doi.org/10.1007/s00254-005-0010-6.
   Web of Science ®Google Scholar
• Yang, Y., Q. Chen, Q. Xu, Y. Zhang, Q. Yong, and G. Liu. 2017. “Coseismic Surface Deformation of the 2014 Napa Earthquake Mapped by Sentinel-1A SAR and Accuracy Assessment with COSMO-SkyMed and GPS Data as Cross Validation.” International Journal of Digital Earth 10 (12): 1197–1213. https://doi.org/10.1080/17538947.2017.1299806.
   Web of Science ®Google Scholar