{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:07:31Z","timestamp":1783523251222,"version":"3.55.0"},"reference-count":92,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,25]],"date-time":"2020-10-25T00:00:00Z","timestamp":1603584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The aims of this research were to map and analyze the risk of land subsidence in the Seoul Metropolitan Area, South Korea using satellite interferometric synthetic aperture radar (InSAR) time-series data, and three ensemble machine-learning models, Bagging, LogitBoost, and Multiclass Classifier. Of the types of infrastructure present in the Seoul Metropolitan Area, subway lines may be vulnerable to land subsidence. In this study, we analyzed Persistent Scatterer InSAR time-series data using the Stanford Method for Persistent Scatterers (StaMPS) algorithm to generate a deformation time-series map. Subsidence occurred at four locations, with a deformation rate that ranged from 6\u201312 mm\/year. Subsidence inventory maps were prepared using deformation time-series data from Sentinel-1. Additionally, 10 potential subsidence-related factors were selected and subjected to Geographic Information System analysis. The relationship between each factor and subsidence occurrence was analyzed by using the frequency ratio. Land subsidence susceptibility maps were generated using Bagging, Multiclass Classifier, and LogitBoost models, and map validation was carried out using the area under the curve (AUC) method. Of the three models, Bagging produced the largest AUC (0.883), with LogitBoost and Multiclass Classifier producing AUCs of 0.871 and 0.856, respectively.<\/jats:p>","DOI":"10.3390\/rs12213505","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T03:51:47Z","timestamp":1603684307000},"page":"3505","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Integration of InSAR Time-Series Data and GIS to Assess Land Subsidence along Subway Lines in the Seoul Metropolitan Area, South Korea"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2998-7999","authenticated-orcid":false,"given":"Muhammad Fulki","family":"Fadhillah","sequence":"first","affiliation":[{"name":"Division of Science Education, Kangwon National University, Gangwon-do, Chuncheon-si  24341, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4301-7801","authenticated-orcid":false,"given":"Arief Rizqiyanto","family":"Achmad","sequence":"additional","affiliation":[{"name":"Division of Science Education, Kangwon National University, Gangwon-do, Chuncheon-si  24341, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7235-3225","authenticated-orcid":false,"given":"Chang-Wook","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Science Education, Kangwon National University, Gangwon-do, Chuncheon-si  24341, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1002\/ldr.2475","article-title":"Geomorphological and Hydrological Effects of Subsidence and Land use Change in Industrial and Urban Areas","volume":"27","author":"Machowski","year":"2016","journal-title":"Land Degrad. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1007\/s12665-016-6141-0","article-title":"Field investigation and analysis of ground sinking development in a metropolitan city, Seoul, Korea","volume":"75","author":"Jo","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lee, H., and Oh, J. (2018). Establishing an ANN-based risk model for ground subsidence along railways. Appl. Sci., 8.","DOI":"10.3390\/app8101936"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1016\/j.proenv.2012.01.440","article-title":"Study on the Effect of Tunnel Excavation on Surface Subsidence Based on GIS Data Management","volume":"12","author":"Yuan","year":"2012","journal-title":"Procedia Environ. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Roccheggiani, M., Piacentini, D., Tirincanti, E., Perissin, D., and Menichetti, M. (2019). Detection and monitoring of tunneling induced ground movements using Sentinel-1 SAR interferometry. Remote Sens., 11.","DOI":"10.3390\/rs11060639"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.enggeo.2010.06.015","article-title":"Assessment of ground subsidence using GIS and the weights-of-evidence model","volume":"115","author":"Oh","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.rse.2011.09.005","article-title":"Monitoring land subsidence and its induced geological hazard with Synthetic Aperture Radar Interferometry: A case study in Morelia, Mexico","volume":"117","author":"Cigna","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"139111","DOI":"10.1016\/j.scitotenv.2020.139111","article-title":"Land subsidence and its relation with groundwater aquifers in Beijing Plain of China","volume":"735","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1130\/0091-7613(1999)027<0483:STUADO>2.3.CO;2","article-title":"Sensing the ups and downs of Las Vegas: InSAR reveals structural control of land subsidence and aquifer-system deformation","volume":"27","author":"Amelung","year":"1999","journal-title":"Geology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s11069-010-9509-6","article-title":"Field-observed phenomena of seismic liquefaction and subsidence during the 2008 Wenchuan earthquake in China","volume":"54","author":"Huang","year":"2010","journal-title":"Nat. Hazards"},{"key":"ref_11","first-page":"1","article-title":"Mexico City subsidence observed with persistent scatterer InSAR","volume":"13","author":"Dixon","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1007\/s10040-012-0892-9","article-title":"Evaluation de la subsidence en consid\u00e9rant les structures constituant les aquif\u00e8res de Shanghai, Chine","volume":"20","author":"Xu","year":"2012","journal-title":"Hydrogeol. J."},{"key":"ref_13","first-page":"232","article-title":"Mapping land subsidence in Jakarta, Indonesia using persistent scatterer interferometry (PSI) technique with ALOS PALSAR","volume":"18","author":"Ng","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.rse.2012.10.015","article-title":"Sinking cities in Indonesia: ALOS PALSAR detects rapid subsidence due to groundwater and gas extraction","volume":"128","author":"Chaussard","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1023\/A:1011144602064","article-title":"Land subsidence of Jakarta (Indonesia) and its geodetic monitoring system","volume":"23","author":"Abidin","year":"2001","journal-title":"Nat. Hazards"},{"key":"ref_16","unstructured":"OECD (2013). Health at a Glance 2013: OECD Indicators, OECD Publishing."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, J.Y., Kwon, K.D., and Raza, M. (2018). Current water uses, related risks, and management options for Seoul megacity, Korea. Environ. Earth Sci., 77.","DOI":"10.1007\/s12665-017-7192-6"},{"key":"ref_18","unstructured":"Hanssen, R.F. (2010). Radar Interferometry: Data Interpretation and Error Analysis, Kluwer Academic."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kang, Y., Zhao, C., Zhang, Q., Lu, Z., and Li, B. (2017). Application of InSAR Techniques to an Analysis of the Guanling Landslide. Remote Sens., 9.","DOI":"10.3390\/rs9101046"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.isprsjprs.2019.08.004","article-title":"Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers","volume":"156","author":"Lu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4043","DOI":"10.1109\/TGRS.2007.906092","article-title":"Urban-target recognition by means of repeated spaceborne SAR images","volume":"45","author":"Perissin","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.enggeo.2017.01.011","article-title":"Quantifying groundwater exploitation induced subsidence in the Rafsanjan plain, southeastern Iran, using InSAR time-series and in situ measurements","volume":"218","author":"Motagh","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1080\/01431161.2011.652311","article-title":"Land subsidence in the Nile Delta of Egypt observed by persistent scatterer interferometry","volume":"3","author":"Aly","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2015.10.011","article-title":"Persistent Scatterer Interferometry: A review","volume":"115","author":"Crosetto","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.isprsjprs.2012.05.010","article-title":"An analysis of terrain properties and the location of surface scatterers from persistent scatterer interferometry","volume":"73","author":"Riddick","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-67989-1","article-title":"Extreme subsidence in a populated city (Mashhad) detected by PSInSAR considering groundwater withdrawal and geotechnical properties","volume":"10","author":"Khorrami","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yazici, B.V., and Tunc Gormus, E. (2020). Investigating persistent scatterer InSAR (PSInSAR) technique efficiency for landslides mapping: A case study in Artvin dam area, in Turkey. Geocarto Int., 1\u201319.","DOI":"10.1080\/10106049.2020.1818854"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7509","DOI":"10.1080\/01431161.2020.1760398","article-title":"Dynamic susceptibility mapping of slow-moving landslides using PSInSAR","volume":"41","author":"Jiaxuan","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10040-015-1349-8","article-title":"Comparison of water-level, extensometric, DInSAR and simulation data for quantification of subsidence in Murcia City (SE Spain)","volume":"24","author":"Tessitore","year":"2016","journal-title":"Hydrogeol. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, H., Feng, G., Xu, B., Yu, Y., Li, Z., Du, Y., and Zhu, J. (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 Sens., 9.","DOI":"10.3390\/rs9101004"},{"key":"ref_31","first-page":"199","article-title":"Lithology-controlled subsidence and seasonal aquifer response in the Bandung basin, Indonesia, observed by synthetic aperture radar interferometry","volume":"32","author":"Khakim","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhou, C., Gong, H., Chen, B., Gao, M., Cao, Q., Cao, J., Duan, L., Zuo, J., and Shi, M. (2020). Land Subsidence Response to Different Land Use Types and Water Resource Utilization in Beijing-Tianjin-Hebei, China. Remote Sens., 12.","DOI":"10.3390\/rs12030457"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1080\/01490419.2019.1698480","article-title":"Seasonal Deformation of Permafrost in Wudaoliang Basin in Qinghai-Tibet Plateau Revealed by StaMPS-InSAR","volume":"43","author":"Lu","year":"2020","journal-title":"Mar. Geod."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jennifer, J.J., Saravanan, S., and Pradhan, B. (2020). Persistent Scatterer Interferometry in the post-event monitoring of the Idukki Landslides. Geocarto Int.","DOI":"10.1080\/10106049.2020.1778101"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tzampoglou, P., and Loupasakis, C. (2017). Mining geohazards susceptibility and risk mapping: The case of the Amyntaio open-pit coal mine, West Macedonia, Greece. Environ. Earth Sci., 76.","DOI":"10.1007\/s12665-017-6866-4"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.3390\/rs5031045","article-title":"Persistent Scatterer Interferometry (PSI) Technique for Landslide Characterization and Monitoring","volume":"5","author":"Tofani","year":"2013","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bianchini, S., Solari, L., Del Soldato, M., Raspini, F., Montalti, R., Ciampalini, A., and Casagli, N. (2019). Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic. Remote Sens., 11.","DOI":"10.3390\/rs11172015"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ng, A.H.M., Wang, H., Dai, Y., Pagli, C., Chen, W., Ge, L., Du, Z., and Zhang, K. (2018). InSAR reveals land deformation at Guangzhou and Foshan, China between 2011 and 2017 with COSMO-SkyMed data. Remote Sens., 10.","DOI":"10.3390\/rs10060813"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.enggeo.2004.06.009","article-title":"Landslide hazard spatial analysis and prediction using GIS in the Xiaojiang watershed, Yunnan, China","volume":"76","author":"Lan","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1007\/s11069-014-1128-1","article-title":"Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS","volume":"73","author":"Pradhan","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s11069-012-0497-6","article-title":"The relationship between geology and rock weathering on the rock instability along Mugling-Narayanghat road corridor, Central Nepal Himalaya","volume":"66","author":"Regmi","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s00267-011-9766-5","article-title":"Spatial prediction of ground subsidence susceptibility using an artificial neural network","volume":"49","author":"Lee","year":"2012","journal-title":"Environ. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"138595","DOI":"10.1016\/j.scitotenv.2020.138595","article-title":"A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility","volume":"726","author":"Arabameri","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.cageo.2012.08.023","article-title":"A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS","volume":"51","author":"Pradhan","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_45","first-page":"422","article-title":"Enhancing the performance of regional land cover mapping","volume":"52","author":"Wu","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Ho, T.C., Revhaug, I., Pradhan, B., and Nguyen, D.B. (2014). Landslide Susceptibility Mapping Along the National Road 32 of Vietnam Using GIS-Based J48 Decision Tree Classifier and Its Ensembles. Cartography from Pole to Pole, Springer.","DOI":"10.1007\/978-3-642-32618-9_22"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kadavi, P.R., Lee, C.W., and Lee, S. (2018). Application of ensemble-based machine learning models to landslide susceptibility mapping. Remote Sens., 10.","DOI":"10.3390\/rs10081252"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"137231","DOI":"10.1016\/j.scitotenv.2020.137231","article-title":"Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble","volume":"718","author":"Hong","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4017","DOI":"10.1007\/s10064-018-1403-6","article-title":"Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions","volume":"78","author":"Abdollahi","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s100400100139","article-title":"Urbanization and the groundwater budget, metropolitan Seoul area, Korea","volume":"9","author":"Kim","year":"2001","journal-title":"Hydrogeol. J."},{"key":"ref_52","unstructured":"Korea, S. (2011). Complete Enumeration Results of the 2010 Population and Housing Census, Statistics Korea."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1007\/s00254-004-1205-y","article-title":"Hydrochemistry of urban groundwater in Seoul, South Korea: Effects of land-use and pollutant recharge","volume":"48","author":"Choi","year":"2005","journal-title":"Environ. Geol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1144\/1470-9236\/07-056","article-title":"Time-series analysis of three years of groundwater level data (Seoul, South Korea) to characterize urban groundwater recharge","volume":"43","author":"Chae","year":"2010","journal-title":"Q. J. Eng. Geol. Hydrogeol."},{"key":"ref_55","unstructured":"Korea, R. (2015). KORAIL Sustainability Report 2015, Korea Railroad Corporation."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., and Roth, L. (2007). The shuttle radar topography mission. Rev. Geophys., 45.","DOI":"10.1029\/2005RG000183"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hooper, A., Segall, P., and Zebker, H. (2007). Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volc\u00e1n Alcedo, Gal\u00e1pagos. J. Geophys. Res. Solid Earth, 112.","DOI":"10.1029\/2006JB004763"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Hooper, A.J. (2008). A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophys. Res. Lett., 35.","DOI":"10.1029\/2008GL034654"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2652","DOI":"10.1016\/j.rse.2011.05.021","article-title":"Persistent Scatterer InSAR: A comparison of methodologies based on a model of temporal deformation vs. spatial correlation selection criteria","volume":"115","author":"Sousa","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Pepe, A., Bonano, M., Zhao, Q., Yang, T., and Wang, H. (2016). The Use of C-\/X-Band Time-Gapped SAR Data and Geotechnical Models for the Study of Shanghai\u2019s Ocean-Reclaimed Lands through the SBAS-DInSAR Technique. Remote Sens., 8.","DOI":"10.20944\/preprints201608.0083.v1"},{"key":"ref_61","first-page":"102115","article-title":"Calculating vertical deformation using a single InSAR pair based on singular value decomposition in mining areas","volume":"92","author":"Ren","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1016\/j.asr.2018.11.008","article-title":"Monitoring of long-term land subsidence from 2003 to 2017 in coastal area of Semarang, Indonesia by SBAS DInSAR analyses using Envisat-ASAR, ALOS-PALSAR, and Sentinel-1A SAR data","volume":"63","author":"Yastika","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.jenvman.2013.04.010","article-title":"Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines","volume":"127","author":"Lee","year":"2013","journal-title":"J. Environ. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Korjus, K., Hebart, M.N., and Vicente, R. (2016). An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0161788"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.agrformet.2018.12.015","article-title":"Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability","volume":"266\u2013267","author":"Jaafari","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1007\/s00254-006-0290-5","article-title":"Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS","volume":"50","author":"Kim","year":"2006","journal-title":"Environ. Geol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40562-019-0140-4","article-title":"Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia","volume":"6","author":"Silalahi","year":"2019","journal-title":"Geosci. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1093\/jigpal\/jzs037","article-title":"Mutating network scans for the assessment of supervised classifier ensembles","volume":"21","author":"Sedano","year":"2013","journal-title":"Log. J. IGPL"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Pourghasemi, H., Gayen, A., Park, S., Lee, C.-W., and Lee, S. (2018). Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and Na\u00efveBayes Machine-Learning Algorithms. Sustainability, 10.","DOI":"10.3390\/su10103697"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.jtbi.2005.05.034","article-title":"Using LogitBoost classifier to predict protein structural classes","volume":"238","author":"Cai","year":"2006","journal-title":"J. Theor. Biol."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Kowsari, K., Brown, D.E., Heidarysafa, M., Meimandi, K.J., Gerber, M.S., and Barnes, L.E. (2017, January 18\u201321). HDLTex: Hierarchical Deep Learning for Text Classification. Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, Cancun, Mexico.","DOI":"10.1109\/ICMLA.2017.0-134"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"3922","DOI":"10.1002\/2014WR016841","article-title":"Geomechanics of subsurface water withdrawal and injection","volume":"51","author":"Gambolati","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"084010","DOI":"10.1088\/1748-9326\/9\/8\/084010","article-title":"Groundwater extraction, land subsidence, and sea-level rise in the Mekong Delta, Vietnam","volume":"9","author":"Erban","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-016-5695-1","article-title":"GIS-based evaluation of mining-induced subsidence susceptibility considering 3D multiple mine drifts and estimated mined panels","volume":"75","author":"Suh","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s11069-018-3323-y","article-title":"Logistic regression model for sinkhole susceptibility due to damaged sewer pipes","volume":"93","author":"Kim","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"751","DOI":"10.2166\/wst.2015.546","article-title":"Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions","volume":"73","author":"Padulano","year":"2016","journal-title":"Water Sci. Technol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.enggeo.2005.02.002","article-title":"Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)","volume":"79","author":"Yesilnacar","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"124070","DOI":"10.1016\/j.jhydrol.2019.124070","article-title":"Assessment of land use and climate change effects on land subsidence using a hydrological model and radar technique","volume":"578","author":"Andaryani","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.scitotenv.2018.03.372","article-title":"The relation between land use and subsidence in the Vietnamese Mekong delta","volume":"634","author":"Minderhoud","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.undsp.2016.07.002","article-title":"Ground settlement during tunneling in groundwater drawdown environment\u2014Influencing factors","volume":"1","author":"Yoo","year":"2016","journal-title":"Undergr. Space"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Lee, S., Tiefenbacher, J.P., and Ngo, P.T.T. (2020). Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran). Remote Sens., 12.","DOI":"10.3390\/rs12030490"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Bell, J.W., Amelung, F., Ferretti, A., Bianchi, M., and Novali, F. (2008). Permanent scatterer InSAR reveals seasonal and long-term aquifer-system response to groundwater pumping and artificial recharge. Water Resour. Res., 44.","DOI":"10.1029\/2007WR006152"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1504\/IJW.2017.081111","article-title":"Lessons from three groundwater disputes in Korea: Lack of comprehensive and integrated investigation","volume":"11","author":"Lee","year":"2017","journal-title":"Int. J. Water"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1002\/hyp.10793","article-title":"Lithological control of land subsidence induced by groundwater withdrawal in new urban AREAS (Granada Basin, SE Spain). Multiband DInSAR monitoring","volume":"30","author":"Notti","year":"2016","journal-title":"Hydrol. Process."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1080\/15481603.2016.1257297","article-title":"Spatiotemporal evolution of land subsidence around a subway using InSAR time-series and the entropy method","volume":"54","author":"Chen","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"4459","DOI":"10.1002\/2013WR014938","article-title":"Estimating temporal changes in hydraulic head using InSAR data in the San Luis Valley, Colorado","volume":"50","author":"Reeves","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Motagh, M., Walter, T.R., Sharifi, M.A., Fielding, E., Schenk, A., Anderssohn, J., and Zschau, J. (2008). Land subsidence in Iran caused by widespread water reservoir overexploitation. Geophys. Res. Lett., 35.","DOI":"10.1029\/2008GL033814"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1080\/01431161.2010.536185","article-title":"Persistent scatterers interferometry hotspot and cluster analysis (PSI-HCA) for detection of extremely slow-moving landslides","volume":"33","author":"Lu","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_91","unstructured":"Seong, J.-H. (2009). The Contiguity Ground and Structures Sinkage Analysis of in City Excavation, Korean Geotechnical Society."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.jenvman.2019.02.020","article-title":"Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities","volume":"236","author":"Rahmati","year":"2019","journal-title":"J. Environ. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3505\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:27:53Z","timestamp":1760178473000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3505"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,25]]},"references-count":92,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213505"],"URL":"https:\/\/doi.org\/10.3390\/rs12213505","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,25]]}}}