{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:23:36Z","timestamp":1780316616907,"version":"3.54.1"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T00:00:00Z","timestamp":1699315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Land subsidence is a hazardous phenomenon that requires accurate prediction to mitigate losses and prevent casualties. This study explores the utilization of the Long Short-Term Memory (LSTM) method for time series prediction of land subsidence, considering various contributing factors such as groundwater levels, soil type and slope, aquifer characteristics, vegetation coverage, land use, depth to the water table, proximity to exploiting wells, distance from rivers, distance from faults, temperature, and wet tropospheric products. Due to the high spatial variability of wet tropospheric parameters, utilizing numerical weather models for extraction is impractical, especially in regions with a sparse network of synoptic stations. This hinders obtaining accurate prediction results because wet tropospheric products play a significant role in subsidence prediction and cannot be ignored in the subsidence prediction process. In this study, Global Navigation Satellite Systems (GNSS) tropospheric products, including Integrated Water Vapor (IWV) and EvapoTranspiration (ET), are employed as alternatives. Two scenarios were considered: one incorporating GNSS products alongside other parameters, and the other relying solely on the remaining parameters in the absence of GNSS tropospheric products. Ground truth data from Interferometric Synthetic Aperture Radar (InSAR) displacement measurements were used for evaluation and testing. The results demonstrated that the inclusion of GNSS tropospheric products significantly enhanced prediction accuracy, with a Root Mean Square Error (RMSE) value of 3.07\u00a0cm\/year in the first scenario. In the second scenario, the absence of wet tropospheric information led to subpar predictions, highlighting the crucial role of wet tropospheric data in spatial distribution. However, by utilizing tropospheric products obtained from GNSS observations, reasonably accurate predictions of displacement changes were achieved. This study underscores the importance of tropospheric indices and showcases the potential of the LSTM method in conjunction with GNSS observations for effective land subsidence prediction, enabling improved preventive measures and mitigation strategies in regions lacking synoptic data coverage.<\/jats:p>","DOI":"10.1007\/s12145-023-01143-z","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T11:02:07Z","timestamp":1699354927000},"page":"3039-3056","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Leveraging GNSS tropospheric products for machine learning-based land subsidence prediction"],"prefix":"10.1007","volume":"16","author":[{"given":"Melika","family":"Tasan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahrasadat","family":"Ghorbaninasab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeid","family":"Haji-Aghajany","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Ghiasvand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,7]]},"reference":[{"key":"1143_CR1","first-page":"3732","volume":"14","author":"G Agnihotri","year":"2021","unstructured":"Agnihotri G, Chaurasia V, Kumar S (2021) A comparative study of different machine learning approaches for Precise Tropospheric Parameter Estimation using GNSS Data. IEEE J Sel Top Appl Earth Observations Remote Sens 14:3732\u20133744","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"issue":"3","key":"1143_CR2","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1109\/LGRS.2011.2170952","volume":"9","author":"V Akbari","year":"2012","unstructured":"Akbari V, Motagh M (2012) Improved ground subsidence monitoring using small baseline SAR interferograms and a weighted least squares inversion algorithm. IEEE Geosci Remote Sens Lett 9(3):437\u2013444","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1143_CR3","volume-title":"Crop evapotranspiration. FAO Irrigation and Drainage, Pa-per No. 56","author":"RG Allen","year":"1998","unstructured":"Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration. FAO Irrigation and Drainage, Pa-per No. 56. Food and Agriculture Organization of the United Nations, Rome"},{"key":"1143_CR4","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1111\/j.1365-246X.2008.03805.x","volume":"74","author":"J Anderssohn","year":"2008","unstructured":"Anderssohn J, Wetzel HL, Walter TR, Motagh M, Djamour Y, Kaufmann H (2008) Land subsidence pattern controlled by old alpine basement faults in the Kashmar Valley, northeast Iran: results from InSAR and levelling. Geophys J Int 74:287\u2013294. https:\/\/doi.org\/10.1111\/j.1365-246X.2008.03805.x","journal-title":"Geophys J Int"},{"key":"1143_CR5","doi-asserted-by":"publisher","unstructured":"Andreas H, Abidin Z, Gumilar H, Sidiq I., P., Sarsito T., A., D., Pradipta D (2018) Insight into the correlation between Land Subsidence and the Floods in regions of Indonesia. IntechOpen. https:\/\/doi.org\/10.5772\/intechopen.80263","DOI":"10.5772\/intechopen.80263"},{"key":"1143_CR6","first-page":"100691","volume":"25","author":"Z Azarakhsh","year":"2022","unstructured":"Azarakhsh Z, Azadbakht M, Matkan A (2022) Estimation, modeling, and prediction of land subsidence using Sentinel-1 time series in Tehran-Shahriar plain: a machine learning-based investigation. Remote Sens Appl Soc Environ 25:100691 [Google Scholar] [CrossRef]","journal-title":"Remote Sens Appl Soc Environ"},{"issue":"11","key":"1143_CR7","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","volume":"40","author":"P Berardino","year":"2002","unstructured":"Berardino P, Fornaro G, Lanari R, Sansosti E (2002) Small baseline subset (SBAS) interferometry: a Novel Method for Monitoring Elevation Changes Applied to volcanoes using ERS synthetic aperture Radar Data. IEEE Trans Geosci Remote Sens 40(11):2375\u20132383","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"D14","key":"1143_CR8","doi-asserted-by":"publisher","first-page":"15787","DOI":"10.1029\/92JD01517","volume":"97","author":"M Bevis","year":"1992","unstructured":"Bevis M, Businger S, Herring TA, Rocken C, Anthes RA, Ware RH (1992) GNSS meteorology: Remote sensing of atmospheric water vapor using the global positioning system. J Geophys Research: Atmos 97(D14):15787\u201315801","journal-title":"J Geophys Research: Atmos"},{"key":"1143_CR9","doi-asserted-by":"crossref","first-page":"D12","DOI":"10.1029\/2001JB000324","volume":"108","author":"O Bock","year":"2003","unstructured":"Bock O, Bouin MN, Walpersdorf A, Doerflinger E (2003) Comparison of GNSS precipitable water vapor to Independent observations and numerical weather prediction models. J Geophys Research: Atmos 108:D12","journal-title":"J Geophys Research: Atmos"},{"issue":"12","key":"1143_CR10","first-page":"771","volume":"80","author":"J Boehm","year":"2006","unstructured":"Boehm J, Heinkelmann R, Schuh H (2006) Short note: a global model of pressure and temperature for geodetic applications. J Geodesy 80(12):771\u2013781","journal-title":"J Geodesy"},{"issue":"19","key":"1143_CR11","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/s12665-019-8552-y","volume":"78","author":"H Chen","year":"2019","unstructured":"Chen H, Luo Z, Zhou Z, Qi S (2019) Investigation on soil moisture and its influence on land subsidence based on remote sensing and field observations in Beijing plain, China. Environ Earth Sci 78(19):575. https:\/\/doi.org\/10.1007\/s12665-019-8552-y","journal-title":"Environ Earth Sci"},{"key":"1143_CR13","doi-asserted-by":"publisher","DOI":"10.7892\/boris.72297","volume-title":"Bernese GNSS Software Version 5.2. User manual","author":"R Dach","year":"2015","unstructured":"Dach R, Lutz S, Walser P, Fridez P (2015) Bernese GNSS Software Version 5.2. User manual. Astronomical Institute, University of Bern, Bern Open Publishing. https:\/\/doi.org\/10.7892\/boris.72297. ISBN: 978-3-906813-05-9"},{"issue":"4","key":"1143_CR12","doi-asserted-by":"crossref","first-page":"660","DOI":"10.3390\/rs12040660","volume":"12","author":"R Dach","year":"2020","unstructured":"Dach R, Arnold D, Grahsl A, Ge M (2020) Global tropospheric maps based on Global Navigation Satellite Systems: a review. Remote Sens 12(4):660","journal-title":"Remote Sens"},{"issue":"2","key":"1143_CR14","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1002\/jgra.50146","volume":"118","author":"JL Davis","year":"2013","unstructured":"Davis JL, Yu Z, Wdowinski S, Zhang P, Lee H (2013) Surface deformations caused by the 2010 Maule Earthquake, Chile: Ground-based and GNSS measurements. J Geophys Research: Solid Earth 118(2):823\u20138303","journal-title":"J Geophys Research: Solid Earth"},{"key":"1143_CR15","doi-asserted-by":"publisher","unstructured":"Davoodijam M, Motagh M, Momeni M (2015) Land subsidence in Mahyar Plain, Central Iran, investigated using Envisat SAR Data. In: Proceedings the 1st international workshop on the quality of geodetic observation and monitoring systems (QuGOMS\u201911). Springer, pp\u00a0127\u2013130. https:\/\/doi.org\/10.1007\/978-3-319-10828","DOI":"10.1007\/978-3-319-10828"},{"key":"1143_CR16","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1111\/j.1365-246X.2009.04135.x","volume":"178","author":"M Dehghani","year":"2009","unstructured":"Dehghani M, Valadan Zoej MJ, Entezam I, Mansourian A, Saatchi S (2009) InSAR monitoring of Progressive land subsidence in Neyshabour, northeast Iran. Geophys J Int 178:47\u201356 [CrossRef]","journal-title":"Geophys J Int"},{"issue":"10","key":"1143_CR17","doi-asserted-by":"publisher","first-page":"5125","DOI":"10.1007\/s10064-020-01872-1","volume":"79","author":"P Ding","year":"2020","unstructured":"Ding P, Jia C, Di S, Wang L, Bian C, Yang X (2020) Analysis and prediction of land subsidence along signifcant linear engineering. Bull Eng Geol Environ 79(10):5125\u20135139","journal-title":"Bull Eng Geol Environ"},{"issue":"2","key":"1143_CR18","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL (1990) Recurrent neural networks. Cogn Sci 14(2):179\u2013211","journal-title":"Cogn Sci"},{"issue":"B5","key":"1143_CR19","doi-asserted-by":"publisher","first-page":"2231","DOI":"10.1029\/2002JB001781","volume":"108","author":"TR Emardson","year":"2003","unstructured":"Emardson TR, Simons M, Webb HH (2003) Neutral atmospheric delay in interferometric synthetic aperture radar applications: statistical description and mitigation. J Geophys Res 108(B5):2231. https:\/\/doi.org\/10.1029\/2002JB001781","journal-title":"J Geophys Res"},{"key":"1143_CR20","unstructured":"European Space Agency (ESA). (n.d.). Sentinel-2. Retrieved from https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-2"},{"key":"1143_CR21","doi-asserted-by":"publisher","unstructured":"Farshbaf A, Mousavi MN, Shahnazi S (2023) Vulnerability assessment of power transmission towers affected by land subsidence via interferometric synthetic aperture radar technique and finite element method analysis: a case study of Zanjan and Qazvin provinces. https:\/\/doi.org\/10.1007\/s10668-023-03127-x. Environ Dev Sustain","DOI":"10.1007\/s10668-023-03127-x"},{"issue":"3","key":"1143_CR22","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10040-015-1339-x","volume":"24","author":"CC Faunt","year":"2016","unstructured":"Faunt CC, Sneed M, Traum J, Brandt JT (2016) Impact of Climate Variability on Land Subsidence in the San Joaquin Valley, California, USA. Hydrogeol J 24(3):675\u2013686","journal-title":"Hydrogeol J"},{"issue":"7","key":"1143_CR23","first-page":"1","volume":"70","author":"A Ferretti","year":"2007","unstructured":"Ferretti A, Prati C, Rocca F (2007) InSAR Time Series Analysis: methods and applications for Earth Surface Deformation Monitoring. Rep Prog Phys 70(7):1\u201374","journal-title":"Rep Prog Phys"},{"issue":"10","key":"1143_CR24","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451\u20132471","journal-title":"Neural Comput"},{"key":"1143_CR25","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press"},{"key":"1143_CR26","unstructured":"Graves A (2013) Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850"},{"key":"1143_CR27","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1029\/2000GL000016","volume":"27","author":"AL Gray","year":"2000","unstructured":"Gray AL, Mattar KE, Sofko G (2000) Influence of ionospheric electron density fluctuations on satellite radar interferometry. Geophys Res Lett 27:1451\u20131454","journal-title":"Geophys Res Lett"},{"issue":"10","key":"1143_CR28","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff K, Srivastava RK, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Networks Learn Syst 28(10):2222\u20132232","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"1143_CR35","unstructured":"Haji-Aghajany S (2021) Function-Based Troposphere Water Vapor Tomography Using GNSS Observations. PhD Thesis, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology"},{"key":"1143_CR33","doi-asserted-by":"publisher","first-page":"39","DOI":"10.22059\/JESPHYS.2018.236885.1006913","volume":"44","author":"S Haji-Aghajany","year":"2018","unstructured":"Haji-Aghajany S, Amerian Y (2018) An investigation of three dimensional ray tracing method efficiency in precise point positioning by tropospheric delay correction. J Earth Space Phys 44:39\u201352. https:\/\/doi.org\/10.22059\/JESPHYS.2018.236885.1006913","journal-title":"J Earth Space Phys"},{"key":"1143_CR31","doi-asserted-by":"publisher","first-page":"105314","DOI":"10.1016\/j.jastp.2020.105314","volume":"209","author":"S Haji-Aghajany","year":"2020","unstructured":"Haji-Aghajany S, Amerian Y (2020) Atmospheric phase screen estimation for land subsidence evaluation by InSAR time series analysis in Kurdistan, Iran. J Atmos Solar Terr Phys 209:105314. https:\/\/doi.org\/10.1016\/j.jastp.2020.105314","journal-title":"J Atmos Solar Terr Phys"},{"issue":"4","key":"1143_CR30","doi-asserted-by":"publisher","first-page":"044503","DOI":"10.1117\/1.JRS.14.044503","volume":"14","author":"S Haji-Aghajany","year":"2020","unstructured":"Haji-Aghajany S, Amerian Y (2020a) Assessment of InSAR tropospheric signal correction methods. J Appl Remote Sens 14(4):044503. https:\/\/doi.org\/10.1117\/1.JRS.14.044503","journal-title":"J Appl Remote Sens"},{"issue":"2","key":"1143_CR32","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1080\/19475705.2017.1289248","volume":"8","author":"S Haji-Aghajany","year":"2017","unstructured":"Haji-Aghajany S, Voosoghi B, Yazdian A (2017) Estimation of North Tabriz Fault parameters using neural networks and 3D tropospherically corrected Surface Displacement Field. Geomatics Nat Hazards Risk 8(2):918\u2013932. https:\/\/doi.org\/10.1080\/19475705.2017.1289248","journal-title":"Geomatics Nat Hazards Risk"},{"issue":"11","key":"1143_CR29","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1016\/j.asr.2019.08.021","volume":"64","author":"S Haji-Aghajany","year":"2019","unstructured":"Haji-Aghajany S, Voosoghi B, Amerian Y (2019) Estimating the slip rate on the north Tabriz fault (Iran) from InSAR measurements with tropospheric correction using 3D ray tracing technique. Adv Space Res 64(11):2199\u20132208. https:\/\/doi.org\/10.1016\/j.asr.2019.08.021","journal-title":"Adv Space Res"},{"issue":"4","key":"1143_CR34","doi-asserted-by":"publisher","first-page":"121","DOI":"10.22059\/JESPHYS.2019.269596.1007065","volume":"45","author":"S Haji-Aghajany","year":"2020","unstructured":"Haji-Aghajany S, Pirooznia M, Raoofian Naeeni M, Amerian Y (2020) Combination of Artificial neural network and genetic algorithm to Inverse Source parameters of Sefid-Sang Earthquake using InSAR technique and Analytical Model Conjunction. J Earth Space Phys 45(4):121\u2013131. https:\/\/doi.org\/10.22059\/JESPHYS.2019.269596.1007065","journal-title":"J Earth Space Phys"},{"issue":"11","key":"1143_CR36","doi-asserted-by":"publisher","first-page":"2548","DOI":"10.3390\/rs14112548","volume":"14","author":"S Haji-Aghajany","year":"2022","unstructured":"Haji-Aghajany S, Amerian Y, Amiri-Simkooei A (2022) Function-based Troposphere tomography technique for optimal downscaling of precipitation. Remote Sens 14(11):2548. https:\/\/doi.org\/10.3390\/rs14112548","journal-title":"Remote Sens"},{"key":"1143_CR37","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.3390\/rs15061555","volume":"15","author":"S Haji-Aghajany","year":"2023","unstructured":"Haji-Aghajany S, Amerian Y, Amiri-Simkooei A (2023) Impact of Climate Change parameters on Groundwater Level: implications for two subsidence regions in Iran using Geodetic observations and Artificial neural networks (ANN). Remote Sens 15:1555. https:\/\/doi.org\/10.3390\/rs15061555","journal-title":"Remote Sens"},{"key":"1143_CR38","doi-asserted-by":"crossref","unstructured":"Hanssen RF (2001) Radar interferometry: data interpretation and error analysis. Springer Science & Business Media","DOI":"10.1007\/0-306-47633-9"},{"key":"1143_CR39","unstructured":"Hersbach H, Dee D ERA5 reanalysis is in production; ECMWF Newsl 147; ECMWF: Reading, UK, 2016."},{"issue":"8","key":"1143_CR40","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"1143_CR41","doi-asserted-by":"publisher","first-page":"106530","DOI":"10.1016\/j.enggeo.2022.106530","volume":"297","author":"J Hu","year":"2022","unstructured":"Hu J, Motagh M, Guo J, Haghighi MH, Li T, Qin F, Wu W (2022) Inferring subsidence characteristics in Wuhan (China) through multitemporal InSAR and hydrogeological analysis. Eng Geol 297:106530","journal-title":"Eng Geol"},{"key":"1143_CR43","doi-asserted-by":"publisher","unstructured":"Jolivet R, Agram PS, Lin NY, Simons M, Doin MP, Peltzer G, Li Z (2014) Improving InSAR geodesy using global atmospheric models. J Geophys Res Solid Earth 119:2324\u20132341.https:\/\/doi.org\/10.1002\/2013JB010588","DOI":"10.1002\/2013JB010588"},{"issue":"2","key":"1143_CR42","doi-asserted-by":"publisher","first-page":"529","DOI":"10.3390\/rs15020529","volume":"15","author":"MA Khalili","year":"2023","unstructured":"Khalili MA, Voosoghi B, Guerriero L, Haji-Aghajany S, Calcaterra D, Di Martire D (2023) Mapping of mean deformation rates based on APS-corrected InSAR data using unsupervised clustering algorithms. Remote Sens 15(2):529. https:\/\/doi.org\/10.3390\/rs15020529","journal-title":"Remote Sens"},{"issue":"4","key":"1143_CR44","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1144\/qjegh2010-069","volume":"45","author":"G Khanlari","year":"2012","unstructured":"Khanlari G, Heidari M, Momeni AA, Ahmadi M, Beydokhti AT (2012) The effect of groundwater overexploitation on land subsidence and sinkhole occurrences, western Iran. Q J Eng GeolHydrogeol 45(4):447\u2013456","journal-title":"Q J Eng GeolHydrogeol"},{"key":"1143_CR45","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"key":"1143_CR46","doi-asserted-by":"crossref","unstructured":"Kleijer F (2004) Troposphere delay modelling and filtering for precise GNSS levelling, Ph.D. thesis, Mathematical Geodesy and Positioning, Delft University of Technology","DOI":"10.54419\/qz77xn"},{"key":"1143_CR47","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1016\/j.techfore.2006.07.010","volume":"74","author":"GD Li","year":"2007","unstructured":"Li GD, Yamaguchi D, Nagai M (2007) A GM(1,1)\u2013Markov chain combined model with an application to predict the number of Chinese international airlines. Technological Forecast Social Change Technol Forecast Soc Change 74:1465\u20131481","journal-title":"Technological Forecast Social Change Technol Forecast Soc Change"},{"key":"1143_CR48","unstructured":"Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019."},{"key":"1143_CR49","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/J.ENGGEO.2015.12.004","volume":"201","author":"M Mahmoudpour","year":"2016","unstructured":"Mahmoudpour M, Khamehchiyan M, Nikudel MR, Ghassemi MR (2016) Numerical simulation and prediction of regional land subsidence caused by groundwater exploitation in the southwest plain of Tehran. Iran Eng Geol 201:6\u201328. https:\/\/doi.org\/10.1016\/J.ENGGEO.2015.12.004","journal-title":"Iran Eng Geol"},{"issue":"6","key":"1143_CR50","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/s12665-017-6559-z","volume":"76","author":"N Mohseni","year":"2017","unstructured":"Mohseni N, Sepehr A, Hosseinzadeh SR, Golzarian MR, Shabani F (2017) Variations in spatial patterns of soil\u2013vegetation properties over subsidence-related ground fissures at an arid ecotone in northeastern Iran. Environ Earth Sci 76(6):234. https:\/\/doi.org\/10.1007\/s12665-017-6559-z","journal-title":"Environ Earth Sci"},{"key":"1143_CR51","doi-asserted-by":"publisher","first-page":"885","DOI":"10.13031\/2013.23153","volume":"50","author":"DN Moriasi","year":"2007","unstructured":"Moriasi DN, Arnold JG, van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for sys-tematic quantification of accuracy in watershed simulations. Trans ASABE 50:885\u2013900","journal-title":"Trans ASABE"},{"key":"1143_CR52","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1029\/2008G","volume":"L0338","author":"M Motagh","year":"2008","unstructured":"Motagh M et al (2008) Land subsidence in Iran caused by widespread water reservoir overexploitation. J Geophys Res Lett L0338:14. https:\/\/doi.org\/10.1029\/2008G","journal-title":"J Geophys Res Lett"},{"key":"1143_CR53","doi-asserted-by":"publisher","unstructured":"Motagh M, Shamshiri R, Haghshenas Haghighi M, Wetzel H-U, Akbari B, Nahavandchi H, Roessner S, Arabi S (2017) Quantifying groundwater exploitation induced subsidence in the Rafsanjan plain, southeastern Iran, using InSAR time-series and in situ measurements. Eng Geol 1\u201318. https:\/\/doi.org\/10.1016\/j.enggeo.2017.01.011","DOI":"10.1016\/j.enggeo.2017.01.011"},{"key":"1143_CR54","doi-asserted-by":"crossref","unstructured":"Paparrizos J, Gravano L (2015) k-shape: Efficient and accurate clustering of time series. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Malbourne, VIC, Australia, 31 May\u20134 June; pp.\u00a01855\u20131870","DOI":"10.1145\/2723372.2737793"},{"issue":"11","key":"1143_CR55","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1130\/0091-7613(2001)029<0975:TSAAFI>2.0.CO;2","volume":"29","author":"G Peltzer","year":"2001","unstructured":"Peltzer G, Cramp\u00e9 F, Hensley S, Rosen P (2001) Transient strain accumulation and fault interaction in the Eastern California shear zone. Geology 29(11):975\u2013978","journal-title":"Geology"},{"key":"1143_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.scitotenv.2019.01.333","volume":"664","author":"Y Qi","year":"2019","unstructured":"Qi Y, Li Q, Karimian H, Liu D (2019) A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Sci Total Environ 664:1\u201310. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.01.333","journal-title":"Sci Total Environ"},{"key":"1143_CR57","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/S0012-821X(03)00331-5","volume":"213","author":"D Remy","year":"2003","unstructured":"Remy D, Bonvalot S, Briole P, Murakami M (2003) Accurate measurement of tropospheric effects in volcanic areas from SAR interferometry data: application to Sakurajima volcano (Japan). Earth Planet Sci Lett 213:299\u2013310","journal-title":"Earth Planet Sci Lett"},{"issue":"3","key":"1143_CR58","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/5.838084","volume":"88","author":"PA Rosen","year":"2000","unstructured":"Rosen PA, Hensley S, Joughin IR (2000) Synthetic aperture radar interferometry. Proc IEEE 88(3):333\u2013382","journal-title":"Proc IEEE"},{"key":"1143_CR59","first-page":"247","volume":"15","author":"J Saastamoinen","year":"1972","unstructured":"Saastamoinen J (1972) Atmospheric correction for the Troposphere and stratosphere in radio ranging of satellites. The Use of Artificial Satellites for Geodesy 15:247\u2013251","journal-title":"The Use of Artificial Satellites for Geodesy"},{"key":"1143_CR60","doi-asserted-by":"crossref","unstructured":"Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In Fifteenth Annual Conference of the International Speech Communication Association","DOI":"10.21437\/Interspeech.2014-80"},{"issue":"19","key":"1143_CR62","doi-asserted-by":"publisher","first-page":"100859","DOI":"10.1016\/j.gsd.2022.100859","volume":"1","author":"OM Sorkhabi","year":"2022","unstructured":"Sorkhabi OM, Kurdpour I, Sarteshnizi RE (2022) Land subsidence and groundwater storage investigation with multi sensor and extended Kalman filter. Groundw Sustain Dev 1(19):100859","journal-title":"Groundw Sustain Dev"},{"key":"1143_CR63","unstructured":"Statistical Center of Iran (2018) Available online: http:\/\/www.amar.org.ir (accessed on 11"},{"key":"1143_CR64","doi-asserted-by":"publisher","first-page":"55","DOI":"10.2307\/210739","volume":"38","author":"CW Thornthwaite","year":"1948","unstructured":"Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55\u201394 [CrossRef]","journal-title":"Geogr Rev"},{"issue":"9","key":"1143_CR65","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1038\/ngeo2246","volume":"7","author":"P Tregoning","year":"2014","unstructured":"Tregoning P, Ramillien G (2014) Water in the balance. Nat Geosci 7(9):613\u2013614","journal-title":"Nat Geosci"},{"key":"1143_CR66","doi-asserted-by":"publisher","first-page":"100680","DOI":"10.1016\/j.gsd.2021.100680","volume":"15","author":"TV Ty","year":"2021","unstructured":"Ty TV, Minh HVT, Avtar R, Kumar P, Hiep HV, Kurasaki M (2021) Spatiotemporal variations in groundwater levels and the impact on land subsidence in CanTho, Vietnam. Groundw Sustain Dev 15:100680. https:\/\/doi.org\/10.1016\/j.gsd.2021.100680","journal-title":"Groundw Sustain Dev"},{"key":"1143_CR67","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1007\/s12205-022-1067-4","volume":"27","author":"H Wang","year":"2023","unstructured":"Wang H, Jia C, Ding P et al (2023) Analysis and prediction of Regional Land Subsidence with InSAR Technology and Machine Learning Algorithm. KSCE J Civ Eng 27:782\u2013793. https:\/\/doi.org\/10.1007\/s12205-022-1067-4","journal-title":"KSCE J Civ Eng"},{"key":"1143_CR68","doi-asserted-by":"crossref","unstructured":"Webster R, Oliver MA (2007) Geostatistics for environmental scientists. John Wiley & Sons","DOI":"10.1002\/9780470517277"},{"issue":"sp1","key":"1143_CR69","doi-asserted-by":"publisher","first-page":"57","DOI":"10.2112\/SI95-011.1","volume":"95","author":"S Ye","year":"2020","unstructured":"Ye S, Zhou G, Peng Y, Liu X, Sun C (2020) Impacts of soil moisture and temperature variations on land subsidence in Shanghai, China. J Coastal Res 95(sp1):57\u201363. https:\/\/doi.org\/10.2112\/SI95-011.1","journal-title":"J Coastal Res"},{"issue":"5","key":"1143_CR70","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1109\/36.175330","volume":"30","author":"HA Zebker","year":"1992","unstructured":"Zebker HA, Villasenor J (1992) Decorrelation in interferometric radar echoes. IEEE Trans Geosci Remote Sens 30(5):950\u2013959","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"1143_CR71","first-page":"951","volume":"90","author":"K Zhang","year":"2016","unstructured":"Zhang K, Lu C, Chen W, Hu C, Zhang J, Jiao W (2016) Zenith wet delay estimation using ground-based GNSS and surface pressure observations: a comparative study. J Geodesy 90(10):951\u2013964","journal-title":"J Geodesy"},{"key":"1143_CR72","doi-asserted-by":"publisher","first-page":"4848","DOI":"10.3390\/rs13234848","volume":"13","author":"Q Zhao","year":"2021","unstructured":"Zhao Q, Sun T, Zhang T, He L, Zhang Z, Shen Z, Xiong S (2021) High-Precision potential evapotranspiration model using GNSS Observation. Remote Sens 13:4848. https:\/\/doi.org\/10.3390\/rs13234848","journal-title":"Remote Sens"},{"key":"1143_CR73","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.3390\/rs14081803","volume":"14","author":"D Zhou","year":"2022","unstructured":"Zhou D, Zuo X, Zhao Z (2022) Constructing a large-scale Urban Land Subsidence Prediction Method based on neural Network Algorithm from the perspective of multiple factors. Remote Sens 14:1803. https:\/\/doi.org\/10.3390\/rs14081803","journal-title":"Remote Sens"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01143-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-023-01143-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-023-01143-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T11:54:05Z","timestamp":1703505245000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-023-01143-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,7]]},"references-count":72,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["1143"],"URL":"https:\/\/doi.org\/10.1007\/s12145-023-01143-z","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,7]]},"assertion":[{"value":"29 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}