{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T22:08:44Z","timestamp":1768687724834,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring","award":["202303AP140015"],"award-info":[{"award-number":["202303AP140015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east\u2013west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.<\/jats:p>","DOI":"10.3390\/s24154770","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T14:26:50Z","timestamp":1721744810000},"page":"4770","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models"],"prefix":"10.3390","volume":"24","author":[{"given":"Kangtai","family":"Chang","sequence":"first","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}]},{"given":"Zhifang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Yunnan University, Kunming 650500, China"},{"name":"Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650500, China"},{"name":"Research Center of Domestic High-Resolution Satellite Remote Sensing Geological Engineering, Kunming 650500, China"},{"name":"Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, China"}]},{"given":"Dingyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}]},{"given":"Zhuyu","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Geological Engineering and Geomatics, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Chang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, D., Deng, W., Yang, T., Li, J., and Zhao, Y. (2023). A Case Study Integrating Numerical Simulation and InSAR Monitoring to Analyze Bedding-Controlled Landslide in Nanfen Open-Pit Mine. Sustainability, 15.","DOI":"10.3390\/su151411158"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Diao, X., Sun, Q., Yang, J., Wu, K., and Lu, X. (2023). A Novel Deformation Extraction Approach for Sub-Band InSAR and Its Application in Large-Scale Surface Mining Subsidence Monitoring. Sustainability, 15.","DOI":"10.3390\/su15010354"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1038\/s41467-022-27997-3","article-title":"Amazon Forests Capture High Levels of Atmospheric Mercury Pollution from Artisanal Gold Mining","volume":"13","author":"Gerson","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, Y., Guo, Y., Hu, S., Li, Y., Wang, J., Liu, X., and Wang, L. (2019). Ground Deformation Analysis Using InSAR and Backpropagation Prediction with Influencing Factors in Erhai Region, China. Sustainability, 11.","DOI":"10.3390\/su11102853"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"125523","DOI":"10.1016\/j.jclepro.2020.125523","article-title":"Construction of Landscape Ecological Network Based on Landscape Ecological Risk Assessment in a Large-Scale Opencast Coal Mine Area","volume":"286","author":"Xu","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_6","first-page":"441","article-title":"Recognition and Prevention of Rockfall Vulnerable Area in Open-Pit Mines Based on Slope Stability Analysis","volume":"26","author":"Zhu","year":"2021","journal-title":"Geomech. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Han, Y., Liu, G., Liu, J., Yang, J., Xie, X., Yan, W., and Zhang, W. (2023). Monitoring and Analysis of Land Subsidence in Jiaozuo City (China) Based on SBAS-InSAR Technology. Sustainability, 15.","DOI":"10.3390\/su151511737"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11069-015-1931-3","article-title":"China\u2019s Regional Social Vulnerability to Geological Disasters: Evaluation and Spatial Characteristics Analysis","volume":"84","author":"Hou","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s10064-023-03186-4","article-title":"Engineering Geology and Mechanism of Multiple Landslides in a Large Open-Pit Mine: The Case of the Copper Mine in Qinghai Province, China","volume":"82","author":"Wang","year":"2023","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, N., Wang, Y., Zhao, F., Wang, T., Zhang, K., Fan, H., Zhou, D., Zhang, L., Yan, S., and Diao, X. (2024). Monitoring and Analysis of the Collapse at Xinjing Open-Pit Mine, Inner Mongolia, China, Using Multi-Source Remote Sensing. Remote Sens., 16.","DOI":"10.3390\/rs16060993"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bazanowski, M., Szostak-Chrzanowski, A., and Chrzanowski, A. (2019). Determination of GPS Session Duration in Ground Deformation Surveys in Mining Areas. Sustainability, 11.","DOI":"10.3390\/su11216127"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106173","DOI":"10.1016\/j.enggeo.2021.106173","article-title":"Searching for Possible Precursors of Mining-Induced Ground Collapse Using Long-Term Geodetic Monitoring Data","volume":"289","author":"Kharisova","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1179\/1752270614Y.0000000092","article-title":"Mine Surface Deformation Monitoring Using Modified GPS RTK with Surveying Rod: Initial Results","volume":"47","author":"Liu","year":"2015","journal-title":"Surv. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112161","DOI":"10.1016\/j.rse.2020.112161","article-title":"Present-Day Land Subsidence Rates, Surface Faulting Hazard and Risk in Mexico City with 2014-2020 Sentinel-1 IW InSAR","volume":"253","author":"Cigna","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1109\/LGRS.2011.2156381","article-title":"InSAR Tropospheric Stratification Delays: Correction Using a Small Baseline Approach","volume":"8","author":"Lauknes","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, J., Ma, F., Li, G., Guo, J., Wan, Y., and Song, Y. (2022). Evolution Assessment of Mining Subsidence Characteristics Using SBAS and PS Interferometry in Sanshandao Gold Mine, China. Remote Sens., 14.","DOI":"10.3390\/rs14020290"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"L09309","DOI":"10.1029\/2007GL029427","article-title":"Mining-Related Ground Deformation in Crescent Valley, Nevada: Implications for Sparse GPS Networks","volume":"34","author":"Gourmelen","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"L05502","DOI":"10.1029\/2010GL046484","article-title":"Using Multiple RADARSAT InSAR Pairs to Estimate a Full Three-Dimensional Solution for Glacial Ice Movement","volume":"38","author":"Gray","year":"2011","journal-title":"Geophys. Res. Lett."},{"key":"ref_19","first-page":"1095","article-title":"Multidimensional Time-Series Analysis of Ground Deformation from Multiple InSAR Data Sets Applied to Virunga Volcanic Province","volume":"191","author":"Samsonov","year":"2012","journal-title":"Geophys. J. Int."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shahzad, N., Ding, X., Wu, S., and Liang, H. (2020). Ground Deformation and Its Causes in Abbottabad City, Pakistan from Sentinel-1A Data and MT-InSAR. Remote Sens., 12.","DOI":"10.3390\/rs12203442"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ma, F., Sui, L., and Lian, W. (2023). Prediction of Mine Subsidence Based on InSAR Technology and the LSTM Algorithm: A Case Study of the Shigouyi Coalfield, Ningxia (China). Remote Sens., 15.","DOI":"10.3390\/rs15112755"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6921","DOI":"10.1080\/01431161.2021.1947540","article-title":"Prediction of InSAR Deformation Time-Series Using a Long Short-Term Memory Neural Network","volume":"42","author":"Chen","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"038505","DOI":"10.1117\/1.JRS.16.038505","article-title":"Time Series Monitoring and Prediction of Coal Mining Subsidence Based on Multitemporal InSAR Technology and GSM-HW Model","volume":"16","author":"Ding","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6732","DOI":"10.1109\/JSTARS.2022.3198728","article-title":"Time-Series Analysis and Prediction of Surface Deformation in the Jinchuan Mining Area, Gansu Province, by Using InSAR and CNN-PhLSTM Network","volume":"15","author":"He","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5512","DOI":"10.1038\/s41598-023-32189-0","article-title":"Prediction Method of Surface Settlement of Rectangular Pipe Jacking Tunnel Based on Improved PSO-BP Neural Network","volume":"13","author":"Hu","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1177\/01445987221107679","article-title":"Improved Mining Subsidence Prediction Model for High Water Level Area Using Machine Learning and Chaos Theory","volume":"40","author":"Yang","year":"2022","journal-title":"Energy Explor. Exploit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109882","DOI":"10.1016\/j.ecolind.2023.109882","article-title":"A Novel Hybrid BPNN Model Based on Adaptive Evolutionary Artificial Bee Colony Algorithm for Water Quality Index Prediction","volume":"146","author":"Chen","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s10661-024-12717-9","article-title":"Comparison of the Monitoring of Surface Deformations in Open-Pit Mines with Sentinel-1A and TerraSAR-X Satellite Radar Data","volume":"196","author":"Guel","year":"2024","journal-title":"Environ. Monit. Assess."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11202","DOI":"10.3390\/rs70911202","article-title":"SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery","volume":"7","author":"Su","year":"2015","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Xie, C., He, Y., Zhu, M., Huang, W., and Shao, T. (2022). Monitoring Potential Geological Hazards with Different InSAR Algorithms: The Case of Western Sichuan. Remote Sens., 14.","DOI":"10.3390\/rs14092049"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Q., Yu, W., Xu, B., and Wei, G. (2019). Assessing the Use of GACOS Products for SBAS-InSAR Deformation Monitoring: A Case in Southern California. Sensors, 19.","DOI":"10.3390\/s19183894"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dittrich, J., Hoelbling, D., Tiede, D., and Saemundsson, T. (2022). Inferring 2D Local Surface-Deformation Velocities Based on PSI Analysis of Sentinel-1 Data: A Case Study of Oraefajokull, Iceland. Remote Sens., 14.","DOI":"10.3390\/rs14133166"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"66878","DOI":"10.1109\/ACCESS.2021.3076065","article-title":"An Improved Multi-Platform Stacked D-InSAR Method for Monitoring the Three-Dimensional Deformation of the Mining Area","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_35","first-page":"895","article-title":"Surface Subsidence Monitoring and Prediction in Mining Area Based on SBAS-InSAR and PSO-BP Neural Network Algorithm","volume":"43","author":"Zhou","year":"2021","journal-title":"J. Yunnan Univ. Nat. Sci. Ed."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1007\/s00500-020-05250-7","article-title":"An Improved Evolution Algorithm Using Population Competition Genetic Algorithm and Self-Correction BP Neural Network Based on Fitness Landscape","volume":"25","author":"Yang","year":"2021","journal-title":"Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"31942","DOI":"10.1007\/s11356-024-33300-2","article-title":"Land Subsidence Prediction in Coal Mining Using Machine Learning Models and Optimization Techniques","volume":"31","author":"Jahanmiri","year":"2024","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"She, X., Li, D., Yang, S., Xie, X., Sun, Y., and Zhao, W. (2024). Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation. Remote Sens., 16.","DOI":"10.3390\/rs16091587"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10005","DOI":"10.1038\/s41598-024-60590-w","article-title":"Analysis of Deformation Mechanism of Rainfall-Induced Landslide in the Three Gorges Reservoir Area: Piansongshu Landslide","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4770\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:21:37Z","timestamp":1760109697000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/4770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,23]]},"references-count":39,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24154770"],"URL":"https:\/\/doi.org\/10.3390\/s24154770","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,23]]}}}