{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T23:27:19Z","timestamp":1786490839969,"version":"3.56.0"},"reference-count":63,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100020595","name":"NSTC","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.eswa.2026.133290","type":"journal-article","created":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T16:51:12Z","timestamp":1781455872000},"page":"133290","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["Modeling time-frequency deep learning to estimate excessive groundwater extraction induced land subsidence along Taiwan high-speed rail corridors"],"prefix":"10.1016","volume":"331","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1312-4822","authenticated-orcid":false,"given":"Min-Yuan","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2282-5487","authenticated-orcid":false,"given":"Akhmad F.K.","family":"Khitam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0142-8461","authenticated-orcid":false,"given":"Quoc-Tuan","family":"Vu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babucarr","family":"Badjie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.133290_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.advwatres.2020.103682","article-title":"Modeling the surface water and groundwater budgets of the US using MODFLOW-OWHM","volume":"143","author":"Alattar","year":"2020","journal-title":"Advances in Water Resources"},{"issue":"4","key":"10.1016\/j.eswa.2026.133290_b0010","doi-asserted-by":"crossref","first-page":"3227","DOI":"10.1007\/s12145-023-01052-1","article-title":"Prediction of groundwater level variations using deep learning methods and GMS numerical model","volume":"16","author":"Amiri","year":"2023","journal-title":"Earth Science Informatics"},{"key":"10.1016\/j.eswa.2026.133290_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2021.112067","article-title":"Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation","volume":"284","author":"Arabameri","year":"2021","journal-title":"Journal of Environmental Management"},{"key":"10.1016\/j.eswa.2026.133290_b0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.gsd.2021.100687","article-title":"Estimation of water level fluctuations in groundwater through a hybrid learning machine","volume":"15","author":"Azizpour","year":"2021","journal-title":"Groundwater for Sustainable Development"},{"issue":"3","key":"10.1016\/j.eswa.2026.133290_b0025","first-page":"411","article-title":"What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models","volume":"66","author":"Babyak","year":"2004","journal-title":"Biopsychosocial Science and Medicine"},{"key":"10.1016\/j.eswa.2026.133290_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2021.146193","article-title":"Land subsidence: A global challenge","volume":"778","author":"Bagheri-Gavkosh","year":"2021","journal-title":"Science of The Total Environment"},{"issue":"23","key":"10.1016\/j.eswa.2026.133290_b0035","doi-asserted-by":"crossref","first-page":"4420","DOI":"10.1002\/hyp.10933","article-title":"Assessing regional-scale spatio-temporal patterns of groundwater\u2013surface water interactions using a coupled SWAT-MODFLOW model","volume":"30","author":"Bailey","year":"2016","journal-title":"Hydrological Processes"},{"issue":"2","key":"10.1016\/j.eswa.2026.133290_b0040","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1111\/j.1745-6584.2009.00644.x","article-title":"Modeling Surface Water-Groundwater Interaction with MODFLOW: Some Considerations","volume":"48","author":"Brunner","year":"2010","journal-title":"Groundwater"},{"key":"10.1016\/j.eswa.2026.133290_b0045","doi-asserted-by":"crossref","unstructured":"Cerqueira, V., Torgo, L., Smailovi\u0107, J., & Mozeti\u010d, I. (2017, 19-21 Oct. 2017). A Comparative Study of Performance Estimation Methods for Time Series Forecasting. Paper presented at the 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). doi: 10.1109\/DSAA.2017.7.","DOI":"10.1109\/DSAA.2017.7"},{"issue":"4","key":"10.1016\/j.eswa.2026.133290_b0050","doi-asserted-by":"crossref","first-page":"3805","DOI":"10.1007\/s10668-019-00344-1","article-title":"Investigation, simulation, identification and prediction of groundwater levels in coastal areas of Purba Midnapur, India, using MODFLOW","volume":"22","author":"Chakraborty","year":"2020","journal-title":"Environment, Development and Sustainability"},{"key":"10.1016\/j.eswa.2026.133290_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2025.132887","article-title":"Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan\u2019s largest alluvial fan","volume":"655","author":"Chang","year":"2025","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.eswa.2026.133290_b0060","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2024.04.021","article-title":"Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks","volume":"212","author":"Che","year":"2024","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.133290_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2020.109869","article-title":"Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project","volume":"138","author":"Cheng","year":"2020","journal-title":"Chaos, Solitons & Fractals"},{"key":"10.1016\/j.eswa.2026.133290_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.jobe.2020.101973","article-title":"Dynamic feature selection for accurately predicting construction productivity using symbiotic organisms search-optimized least square support vector machine","volume":"35","author":"Cheng","year":"2021","journal-title":"Journal of Building Engineering"},{"issue":"1","key":"10.1016\/j.eswa.2026.133290_b0075","article-title":"Self-Tuning Inference Model for Settlement in Shield Tunneling: A Case Study of the Taipei Mass Rapid Transit System\u2019s Songshan Line","volume":"2023","author":"Cheng","year":"2023","journal-title":"Structural Control and Health Monitoring"},{"key":"10.1016\/j.eswa.2026.133290_b0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.apm.2025.116008","article-title":"Artificial satellite search: A new metaheuristic algorithm for optimizing truss structure design and project scheduling","volume":"143","author":"Cheng","year":"2025","journal-title":"Applied Mathematical Modelling"},{"key":"10.1016\/j.eswa.2026.133290_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2025.120020","article-title":"Revolving Gate Fourier Transform (RGFT): A novel time-frequency model for forecasting energy efficiency and CO2 neutrality","volume":"341","author":"Cheng","year":"2025","journal-title":"Energy Conversion and Management"},{"key":"10.1016\/j.eswa.2026.133290_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2024.105904","article-title":"Hybrid deep learning model for accurate cost and schedule estimation in construction projects using sequential and non-sequential data","volume":"170","author":"Cheng","year":"2025","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.133290_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129934","article-title":"Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning","volume":"624","author":"Deng","year":"2023","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.eswa.2026.133290_b0100","doi-asserted-by":"crossref","unstructured":"Dey, R., & Salem, F. M. (2017, 6-9 Aug. 2017). Gate-variants of Gated Recurrent Unit (GRU) neural networks. Paper presented at the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). doi: 10.1109\/MWSCAS.2017.8053243.","DOI":"10.1109\/MWSCAS.2017.8053243"},{"issue":"6","key":"10.1016\/j.eswa.2026.133290_b0105","doi-asserted-by":"crossref","DOI":"10.1029\/2011WR010617","article-title":"Over-extraction from shallow bedrock versus deep alluvial aquifers: Reliability versus sustainability considerations for India's groundwater irrigation","volume":"47","author":"Fishman","year":"2011","journal-title":"Water Resources Research"},{"issue":"2","key":"10.1016\/j.eswa.2026.133290_b0110","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1007\/s11069-018-3449-y","article-title":"A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping","volume":"94","author":"Ghorbanzadeh","year":"2018","journal-title":"Natural Hazards"},{"issue":"3","key":"10.1016\/j.eswa.2026.133290_b0115","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1111\/wej.12688","article-title":"Consequences of groundwater overexploitation on land subsidence in Fars Province of Iran and its mitigation management programme","volume":"35","author":"Golian","year":"2021","journal-title":"Water and Environment Journal"},{"key":"10.1016\/j.eswa.2026.133290_b0120","article-title":"Convolutional neural network and long short-term memory algorithms for groundwater potential mapping in Anseong, South Korea","volume":"39","author":"Hakim","year":"2022","journal-title":"Journal of Hydrology: Regional Studies"},{"key":"10.1016\/j.eswa.2026.133290_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2024.141152","article-title":"Sustainable groundwater management in coastal cities: Insights from groundwater potential and vulnerability using ensemble learning and knowledge-driven models","volume":"442","author":"Huang","year":"2024","journal-title":"Journal of Cleaner Production"},{"issue":"1","key":"10.1016\/j.eswa.2026.133290_b0130","doi-asserted-by":"crossref","DOI":"10.3390\/rs10010040","article-title":"Land Subsidence in Chiayi, Taiwan, from Compaction well, Leveling and ALOS\/PALSAR: Aquaculture-Induced Relative Sea Level rise","volume":"10","author":"Hung","year":"2018","journal-title":"Remote Sensing"},{"key":"10.1016\/j.eswa.2026.133290_b0135","doi-asserted-by":"crossref","unstructured":"Kardan Moghaddam, H., Ghordoyee Milan, S., Kayhomayoon, Z., Rahimzadeh kivi, Z., & Arya Azar, N. (2021). The prediction of aquifer groundwater level based on spatial clustering approach using machine learning. Environmental Monitoring and Assessment, 193(4), 173. doi: 10.1007\/s10661-021-08961-y.","DOI":"10.1007\/s10661-021-08961-y"},{"key":"10.1016\/j.eswa.2026.133290_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2021.113237","article-title":"Novel approach for predicting groundwater storage loss using machine learning","volume":"296","author":"Kayhomayoon","year":"2021","journal-title":"Journal of Environmental Management"},{"issue":"1","key":"10.1016\/j.eswa.2026.133290_b0145","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s10040-014-1204-3","article-title":"Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models","volume":"23","author":"Khalil","year":"2015","journal-title":"Hydrogeology Journal"},{"key":"10.1016\/j.eswa.2026.133290_b0150","unstructured":"Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., & Anandkumar, A. (2020). Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895. doi: 10.48550\/arXiv.2010.08895."},{"issue":"1","key":"10.1016\/j.eswa.2026.133290_b0155","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1109\/TCE.2024.3511994","article-title":"Fusing Interaction transition Features and Cross-attention for Knowledge Tracing in E-Learning Systems","volume":"71","author":"Liu","year":"2025","journal-title":"IEEE Transactions on Consumer Electronics"},{"key":"10.1016\/j.eswa.2026.133290_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.169502","article-title":"Projection of land susceptibility to subsidence hazard in China using an interpretable CNN deep learning model","volume":"913","author":"Liu","year":"2024","journal-title":"Science of The Total Environment"},{"key":"10.1016\/j.eswa.2026.133290_b0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.jag.2023.103228","article-title":"Land subsidence modeling and assessment in the West Pearl River Delta from combined InSAR time series, land use and geological data","volume":"118","author":"Liu","year":"2023","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"issue":"15","key":"10.1016\/j.eswa.2026.133290_b0170","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1007\/s12665-019-8497-4","article-title":"Assessment of groundwater depletion caused by excessive extraction through groundwater flow modeling: The Celaya aquifer in central Mexico","volume":"78","author":"L\u00f3pez-Alvis","year":"2019","journal-title":"Environmental Earth Sciences"},{"key":"10.1016\/j.eswa.2026.133290_b0175","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.enggeo.2015.12.004","article-title":"Numerical simulation and prediction of regional land subsidence caused by groundwater exploitation in the southwest plain of Tehran","volume":"201","author":"Mahmoudpour","year":"2016","journal-title":"Iran. Engineering Geology"},{"key":"10.1016\/j.eswa.2026.133290_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.gsd.2020.100484","article-title":"Modelling groundwater level fluctuations in urban areas using artificial neural network","volume":"12","author":"Malik","year":"2021","journal-title":"Groundwater for Sustainable Development"},{"key":"10.1016\/j.eswa.2026.133290_b0185","unstructured":"Ministry of Environment, T. (2025). Groundwater Environment. Retrieved from https:\/\/einfo.stsp.gov.tw\/einfo\/page\/about\/61."},{"key":"10.1016\/j.eswa.2026.133290_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.gsd.2024.101397","article-title":"Groundwater level forecasting using empirical mode decomposition and wavelet-based long short-term memory (LSTM) neural networks","volume":"28","author":"Nazari","year":"2025","journal-title":"Groundwater for Sustainable Development"},{"issue":"10","key":"10.1016\/j.eswa.2026.133290_b0195","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1007\/s00477-022-02181-7","article-title":"Deep learning-based uncertainty quantification of groundwater level predictions","volume":"36","author":"Nourani","year":"2022","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"issue":"7","key":"10.1016\/j.eswa.2026.133290_b0200","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1007\/s12665-012-1630-2","article-title":"Application of MODFLOW for simulating groundwater flow in the Trifilia karst aquifer","volume":"67","author":"Panagopoulos","year":"2012","journal-title":"Greece. Environmental Earth Sciences"},{"issue":"4","key":"10.1016\/j.eswa.2026.133290_b0205","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.1007\/s10706-019-00837-w","article-title":"Development of an uncertainty based Model to Predict Land Subsidence Caused by Groundwater Extraction (Case Study: Tehran Basin)","volume":"37","author":"Ranjbar","year":"2019","journal-title":"Geotechnical and Geological Engineering"},{"key":"10.1016\/j.eswa.2026.133290_b0210","article-title":"Assessing the vulnerability of Iran to subsidence hazard using a hierarchical FUCOM-GIS framework","volume":"31","author":"Sadeghi","year":"2023","journal-title":"Remote Sensing Applications: Society and Environment"},{"key":"10.1016\/j.eswa.2026.133290_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2021.126800","article-title":"Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations","volume":"601","author":"Solgi","year":"2021","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.eswa.2026.133290_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.131250","article-title":"Deep dive into predictive excellence: Transformer's impact on groundwater level prediction","volume":"636","author":"Sun","year":"2024","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.eswa.2026.133290_b0225","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.neucom.2014.05.026","article-title":"An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India","volume":"145","author":"Suryanarayana","year":"2014","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.133290_b0230","doi-asserted-by":"crossref","unstructured":"Tapoglou, E., C., T. I., Zoi, D., K., N. I., & and Karatzas, G. P. (2014). Groundwater-level forecasting under climate change scenarios using an artificial neural network trained with particle swarm optimization. Hydrological Sciences Journal, 59(6), 1225-1239. doi: 10.1080\/02626667.2013.838005.","DOI":"10.1080\/02626667.2013.838005"},{"issue":"2","key":"10.1016\/j.eswa.2026.133290_b0235","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1007\/s11069-018-3431-8","article-title":"A hybrid clustering-fusion methodology for land subsidence estimation","volume":"94","author":"Taravatrooy","year":"2018","journal-title":"Natural Hazards"},{"key":"10.1016\/j.eswa.2026.133290_b0240","article-title":"Evaluating the predictive power of different machine learning algorithms for groundwater salinity prediction of multi-layer coastal aquifers in the Mekong Delta","volume":"127","author":"Tran","year":"2021","journal-title":"Vietnam. Ecological Indicators"},{"key":"10.1016\/j.eswa.2026.133290_b0245","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.tecto.2012.08.009","article-title":"Assessments of serious anthropogenic land subsidence in Yunlin County of central Taiwan from 1996 to 1999 by Persistent Scatterers InSAR","volume":"578","author":"Tung","year":"2012","journal-title":"Tectonophysics"},{"issue":"1","key":"10.1016\/j.eswa.2026.133290_b0250","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s12205-023-2457-y","article-title":"A Hybrid Framework Combining LSTM NN and BNN for Short-term Traffic Flow Prediction and uncertainty Quantification","volume":"28","author":"Wang","year":"2024","journal-title":"KSCE Journal of Civil Engineering"},{"key":"10.1016\/j.eswa.2026.133290_b0255","unstructured":"Water Resources Agency, M. o. E. A., Taiwan. (2021). Groundwater Monitoring Well Condition Dataset. Retrieved from: https:\/\/data.nat.gov.tw\/dataset\/32718."},{"issue":"3","key":"10.1016\/j.eswa.2026.133290_b0260","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1007\/s11069-023-06198-1","article-title":"Susceptibility assessment of earth fissure related to groundwater extraction using machine learning methods combined with weights of evidence","volume":"119","author":"Wei","year":"2023","journal-title":"Natural Hazards"},{"issue":"6","key":"10.1016\/j.eswa.2026.133290_b0265","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1007\/s10706-018-0558-z","article-title":"Predictive Modeling of Mining Induced Ground Subsidence with Survival Analysis and Online Sequential Extreme Learning Machine","volume":"36","author":"Wei","year":"2018","journal-title":"Geotechnical and Geological Engineering"},{"issue":"4","key":"10.1016\/j.eswa.2026.133290_b0270","doi-asserted-by":"crossref","DOI":"10.3390\/w15040823","article-title":"Predicting Groundwater Level based on Machine Learning: A Case Study of the Hebei Plain","volume":"15","author":"Wu","year":"2023","journal-title":"Water"},{"key":"10.1016\/j.eswa.2026.133290_b0275","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.jhydrol.2011.07.002","article-title":"Integration of SWAP and MODFLOW-2000 for modeling groundwater dynamics in shallow water table areas","volume":"412\u2013413","author":"Xu","year":"2012","journal-title":"Journal of Hydrology"},{"key":"10.1016\/j.eswa.2026.133290_b0280","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neucom.2020.01.106","article-title":"LLR: Learning learning rates by LSTM for training neural networks","volume":"394","author":"Yu","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.133290_b0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.114177","article-title":"Method to enhance time series rolling fault prediction by deep fast Fourier convolution","volume":"228","author":"Yu","year":"2024","journal-title":"Measurement"},{"issue":"2","key":"10.1016\/j.eswa.2026.133290_b0290","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1007\/s11053-019-09490-9","article-title":"Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility using Machine Learning Algorithms","volume":"29","author":"Zamanirad","year":"2020","journal-title":"Natural Resources Research"},{"issue":"6","key":"10.1016\/j.eswa.2026.133290_b0295","doi-asserted-by":"crossref","first-page":"5899","DOI":"10.1007\/s12145-024-01487-0","article-title":"Time series land subsidence monitoring and prediction based on SBAS-InSAR and GeoTemporal transformer model","volume":"17","author":"Zhang","year":"2024","journal-title":"Earth Science Informatics"},{"key":"10.1016\/j.eswa.2026.133290_b0300","unstructured":"Zhang, J., Lei, Q., & Dhillon, I. (2018). Stabilizing gradients for deep neural networks via efficient svd parameterization. Paper presented at the International Conference on Machine Learning. Retrieved from https:\/\/proceedings.mlr.press\/v80\/zhang18g.html."},{"key":"10.1016\/j.eswa.2026.133290_b0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2024.132038","article-title":"A multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models","volume":"643","author":"Zhu","year":"2024","journal-title":"Journal of Hydrology"},{"issue":"2","key":"10.1016\/j.eswa.2026.133290_b0310","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s11769-013-0589-6","article-title":"Comprehensive analysis and artificial intelligent simulation of land subsidence of Beijing","volume":"23","author":"Zhu","year":"2013","journal-title":"China. Chinese Geographical Science"},{"issue":"12","key":"10.1016\/j.eswa.2026.133290_b0315","doi-asserted-by":"crossref","first-page":"2978","DOI":"10.3390\/rs15122978","article-title":"Study on land subsidence simulation based on a back-propagation neural network combined with the sparrow search algorithm","volume":"15","author":"Zhu","year":"2023","journal-title":"Remote Sensing"}],"updated-by":[{"DOI":"10.1016\/j.eswa.2026.133692","type":"erratum","label":"Erratum","source":"publisher","updated":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000}}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426021998?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426021998?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T22:28:18Z","timestamp":1786487298000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426021998"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":63,"alternative-id":["S0957417426021998"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133290","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Modeling time-frequency deep learning to estimate excessive groundwater extraction induced land subsidence along Taiwan high-speed rail corridors","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133290","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133290"}}