{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:37:04Z","timestamp":1767847024902,"version":"3.49.0"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:00:00Z","timestamp":1746230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s12145-025-01856-3","type":"journal-article","created":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T00:17:22Z","timestamp":1746231442000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting groundwater levels in coastal aquifers using deep learning models: a comparative study of sedimentary and metamorphic aquifers in nova scotia"],"prefix":"10.1007","volume":"18","author":[{"given":"Saeideh","family":"Samani","sequence":"first","affiliation":[]},{"given":"Meysam","family":"Vadiati","sequence":"additional","affiliation":[]},{"given":"Ozgur","family":"Kisi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,3]]},"reference":[{"issue":"1","key":"1856_CR1","first-page":"9480522","volume":"2024","author":"ASA Ali","year":"2024","unstructured":"Ali ASA, Jazaei F, Babakhani P, Ashiq MM, Bakhshaee A, Waldron B (2024) An overview of deep learning applications in groundwater level modeling: bridging the gap between academic research and industry applications. Appl Comput Intell Soft Comput 2024(1):9480522","journal-title":"Appl Comput Intell Soft Comput"},{"key":"1856_CR2","unstructured":"Baechler FE, LeBlanc HGJ, Abbott AH (1986) Regional water resources sydney coalfield, Nova Scotia"},{"issue":"5","key":"1856_CR3","first-page":"2023","volume":"8","author":"JP Bharadiya","year":"2023","unstructured":"Bharadiya JP (2023) Exploring the use of recurrent neural networks for time series forecasting. Int J Innov Sci Res Technol 8(5):2023\u20132027","journal-title":"Int J Innov Sci Res Technol"},{"key":"1856_CR4","doi-asserted-by":"publisher","first-page":"24235","DOI":"10.1007\/s11356-024-32706-2","volume":"31","author":"M Bordbar","year":"2024","unstructured":"Bordbar M, Heggy E, Jun C, Bateni SM, Kim D, Moghaddam HK, Rezaie F (2024) Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms. Environ Sci Pollut Res 31:24235","journal-title":"Environ Sci Pollut Res"},{"issue":"5","key":"1856_CR5","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.3390\/w11051098","volume":"11","author":"BD Bowes","year":"2019","unstructured":"Bowes BD, Sadler JM, Morsy MM, Behl M, Goodall JL (2019) Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks. Water 11(5):1098","journal-title":"Water"},{"key":"1856_CR6","first-page":"100930","volume":"37","author":"H Cai","year":"2021","unstructured":"Cai H, Shi H, Liu S, Babovic V (2021) Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States. J Hydrol: Reg Stud 37","journal-title":"J Hydrol: Reg Stud"},{"issue":"4","key":"1856_CR7","doi-asserted-by":"publisher","first-page":"163","DOI":"10.3390\/a17040163","volume":"17","author":"Y Chen","year":"2024","unstructured":"Chen Y, Khaliq A (2024) Quantum recurrent neural networks: predicting the dynamics of oscillatory and chaotic systems. Algorithms 17(4):163","journal-title":"Algorithms"},{"issue":"17","key":"1856_CR8","doi-asserted-by":"publisher","first-page":"3118","DOI":"10.3390\/w15173118","volume":"15","author":"HY Chen","year":"2023","unstructured":"Chen HY, Vojinovic Z, Lo W, Lee JW (2023) Groundwater level prediction with deep learning methods. Water 15(17):3118","journal-title":"Water"},{"key":"1856_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsames.2022.103872","volume":"117","author":"R da Silva Alves","year":"2022","unstructured":"da Silva Alves R, de Lucena LRF (2022) Numerical modeling of NE Brazil coastal aquifer: fault controlled conduits for seawater intrusion. J S Am Earth Sci 117:103872","journal-title":"J S Am Earth Sci"},{"key":"1856_CR10","doi-asserted-by":"crossref","unstructured":"Das S, Tariq A, Santos T, Kantareddy SS, Banerjee I (2023) Recurrent neural networks (RNNs): architectures, training tricks, and introduction to influential research. Machine Learning for Brain Disorders, pp 117\u2013138","DOI":"10.1007\/978-1-0716-3195-9_4"},{"issue":"11","key":"1856_CR11","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1038\/s41583-023-00740-7","volume":"24","author":"D Durstewitz","year":"2023","unstructured":"Durstewitz D, Koppe G, Thurm MI (2023) Reconstructing computational system dynamics from neural data with recurrent neural networks. Nat Rev Neurosci 24(11):693\u2013710","journal-title":"Nat Rev Neurosci"},{"key":"1856_CR12","doi-asserted-by":"crossref","unstructured":"Dutta S, Wagner AM, Hall TK, Pradhan NR (2024) Data-driven modeling of groundwater level using machine learning, ERDC TN-24-3","DOI":"10.21079\/11681\/48452"},{"key":"1856_CR13","doi-asserted-by":"publisher","first-page":"493","DOI":"10.5194\/isprs-annals-X-1-W1-2023-493-2023","volume":"10","author":"Z Eghrari","year":"2023","unstructured":"Eghrari Z, Delavar MR, Zare M, Mousavi M, Nazari B, Ghaffarian S (2023) Groundwater Level Prediction Using Deep Recurrent Neural Networks and Uncertainty Assessment. ISPRS Ann Photogrammetry, Remote Sens Spatial Info Sci 10:493\u2013500","journal-title":"ISPRS Ann Photogrammetry, Remote Sens Spatial Info Sci"},{"key":"1856_CR14","first-page":"366","volume-title":"International Conference on Artificial Intelligence & Industrial Applications","author":"FZ El-Hassani","year":"2023","unstructured":"El-Hassani FZ, Ghanou Y, Haddouch K (2023) A novel model for optimizing multilayer perceptron neural network architecture based on genetic algorithm method. International Conference on Artificial Intelligence & Industrial Applications. Cham, Springer Nature Switzerland, pp 366\u2013380"},{"key":"1856_CR15","doi-asserted-by":"publisher","first-page":"158760","DOI":"10.1016\/j.scitotenv.2022.158760","volume":"854","author":"S Fahad","year":"2023","unstructured":"Fahad S, Su F, Khan SU, Naeem MR, Wei K (2023) Implementing a novel deep learning technique for rainfall forecasting via climatic variables: An approach via hierarchical clustering analysis. Sci Total Environ 854","journal-title":"Sci Total Environ"},{"key":"1856_CR16","unstructured":"Ghyben W (1889) Nota in verband met de voogenomen put boring nabij Amsterdam (Notes on the probable results of the proposed well drilling near Amsterdam). Tijdshrift van het koninklyk Instituut van Ingenieurs 21 (1889). Tijdschr Kon Inst Ing&nbsp, pp 8\u201322"},{"key":"1856_CR17","doi-asserted-by":"crossref","unstructured":"Graves A (2012) Supervised sequence labelling. In: Supervised sequence labelling with recurrent neural networks. Springer, Heidelberg, pp 5\u201313","DOI":"10.1007\/978-3-642-24797-2_2"},{"key":"1856_CR18","first-page":"815","volume":"44","author":"A Herzberg","year":"1901","unstructured":"Herzberg A (1901) Die wasserversorgung einiger Nordseebader. J Gasbeleucht Wasserversorg 44:815\u2013819","journal-title":"J Gasbeleucht Wasserversorg"},{"issue":"8","key":"1856_CR19","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","journal-title":"Neural Comput"},{"issue":"9","key":"1856_CR20","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.3390\/w11091879","volume":"11","author":"X Huang","year":"2019","unstructured":"Huang X, Gao L, Crosbie RS, Zhang N, Fu G, Doble R (2019) Groundwater recharge prediction using linear regression, multi-layer perception network, and deep learning. Water 11(9):1879","journal-title":"Water"},{"issue":"6","key":"1856_CR21","doi-asserted-by":"publisher","first-page":"e2024WR037244","DOI":"10.1029\/2024WR037244","volume":"60","author":"X Huang","year":"2024","unstructured":"Huang X, Werner AD, Sol\u00f3rzano-Rivas SC, Jazayeri A (2024) Salinity profiles in coastal aquifers: A characterization framework for field measurements. Water Resour Res 60(6):e2024WR037244","journal-title":"Water Resour Res"},{"issue":"2","key":"1856_CR22","doi-asserted-by":"publisher","first-page":"259","DOI":"10.3390\/jmse11020259","volume":"11","author":"RMA Ikram","year":"2023","unstructured":"Ikram RMA, Mostafa RR, Chen Z, Parmar KS, Kisi O, Zounemat-Kermani M (2023) Water temperature prediction using improved deep learning methods through reptile search algorithm and weighted mean of vectors optimizer. J Marine Sci Eng 11(2):259","journal-title":"J Marine Sci Eng"},{"key":"1856_CR23","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.jhydrol.2019.02.051","volume":"572","author":"J Jeong","year":"2019","unstructured":"Jeong J, Park E (2019) Comparative applications of data-driven models representing water table fluctuations. J Hydrol 572:261\u2013273","journal-title":"J Hydrol"},{"issue":"4","key":"1856_CR24","doi-asserted-by":"publisher","first-page":"2743","DOI":"10.3390\/app13042743","volume":"13","author":"J Khan","year":"2023","unstructured":"Khan J, Lee E, Balobaid AS, Kim K (2023) A comprehensive review of conventional, machine leaning, and deep learning models for groundwater level (GWL) forecasting. Appl Sci 13(4):2743","journal-title":"Appl Sci"},{"issue":"11","key":"1856_CR25","doi-asserted-by":"publisher","first-page":"6005","DOI":"10.5194\/hess-22-6005-2018","volume":"22","author":"F Kratzert","year":"2018","unstructured":"Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M (2018) Rainfall\u2013runoff modelling using long short-term memory (LSTM) networks. Hydrol Earth Syst Sci 22(11):6005\u20136022","journal-title":"Hydrol Earth Syst Sci"},{"issue":"4","key":"1856_CR26","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1007\/s12145-020-00508-y","volume":"13","author":"D Kumar","year":"2020","unstructured":"Kumar D, Roshni T, Singh A, Jha MK, Samui P (2020) Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study. Earth Sci Inform 13(4):1237\u20131250","journal-title":"Earth Sci Inform"},{"issue":"4","key":"1856_CR27","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4):541\u2013551","journal-title":"Neural Computation"},{"key":"1856_CR28","doi-asserted-by":"publisher","first-page":"106000","DOI":"10.1016\/j.engappai.2023.106000","volume":"121","author":"J Liu","year":"2023","unstructured":"Liu J, Wang X, Xie F, Wu S, Li D (2023) Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network. Eng App Artif Intell 121","journal-title":"Eng App Artif Intell"},{"key":"1856_CR29","doi-asserted-by":"publisher","first-page":"119951","DOI":"10.1016\/j.ins.2023.119951","volume":"657","author":"M Lu","year":"2024","unstructured":"Lu M, Xu X (2024) TRNN: An efficient time-series recurrent neural network for stock price prediction. Inform Sci 657","journal-title":"Inform Sci"},{"key":"1856_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.126100","volume":"263","author":"SX Lv","year":"2023","unstructured":"Lv SX, Wang L (2023) Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model. Energy 263:126100","journal-title":"Energy"},{"issue":"4","key":"1856_CR31","doi-asserted-by":"publisher","first-page":"49","DOI":"10.3390\/hydrology11040049","volume":"11","author":"A Lyra","year":"2024","unstructured":"Lyra A, Loukas A, Sidiropoulos P, Mylopoulos N (2024) Climatic modeling of seawater intrusion in coastal aquifers: understanding the climate change impacts. Hydrology 11(4):49","journal-title":"Hydrology"},{"issue":"6","key":"1856_CR32","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1021\/acsestengg.0c00238","volume":"1","author":"P Malakar","year":"2021","unstructured":"Malakar P, Mukherjee A, Bhanja SN, Sarkar S, Saha D, Ray RK (2021) Deep learning-based forecasting of groundwater level trends in India: Implications for crop production and drinking water supply. ACS ES&T Engineering 1(6):965\u2013977","journal-title":"ACS ES&T Engineering"},{"issue":"9","key":"1856_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3649448","volume":"56","author":"N Mohammadi Foumani","year":"2024","unstructured":"Mohammadi Foumani N, Miller L, Tan CW, Webb GI, Forestier G, Salehi M (2024) Deep learning for time series classification and extrinsic regression: A current survey. ACM Comput Surv 56(9):1\u201345","journal-title":"ACM Comput Surv"},{"issue":"7","key":"1856_CR34","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.3390\/ma17071452","volume":"17","author":"P My\u015bliwiec","year":"2024","unstructured":"My\u015bliwiec P, Kubit A, Szawara P (2024) Optimization of 2024\u2013T3 aluminum alloy friction stir welding using random forest, xgboost, and mlp machine learning techniques. Materials 17(7):1452","journal-title":"Materials"},{"key":"1856_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.marpolbul.2023.115669","volume":"197","author":"AA Nadiri","year":"2023","unstructured":"Nadiri AA, Bordbar M, Nikoo MR, Silabi LSS, Senapathi V, Xiao Y (2023) Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network. Mar Pollut Bull 197:115669","journal-title":"Mar Pollut Bull"},{"issue":"10","key":"1856_CR36","doi-asserted-by":"publisher","first-page":"3081","DOI":"10.1007\/s00477-022-02181-7","volume":"36","author":"V Nourani","year":"2022","unstructured":"Nourani V, Khodkar K, Paknezhad NJ, Laux P (2022) Deep learning-based uncertainty quantification of groundwater level predictions. Stochastic Environ Res Risk Assess 36(10):3081\u20133107","journal-title":"Stochastic Environ Res Risk Assess"},{"key":"1856_CR37","unstructured":"NSE -Nova Scotia environment (2015) The nova scotia groundwater observation well network. https:\/\/novascotia.ca\/nse\/groundwater\/docs\/GroundwaterObservationWellNetwork2015Report.pdf"},{"key":"1856_CR38","unstructured":"Porter RJ (1982) Regional water resources of Southwestern Nova Scotia. Nova Scotia Department of the Environment, Halifax, NS, p 87"},{"issue":"7","key":"1856_CR39","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.5194\/hess-26-1727-2022","volume":"26","author":"H Ren","year":"2022","unstructured":"Ren H, Cromwell E, Kravitz B, Chen X (2022) Using long short-term memory models to fill data gaps in hydrological monitoring networks. Hydrol Earth Syst Sci 26(7):1727\u20131743","journal-title":"Hydrol Earth Syst Sci"},{"key":"1856_CR40","doi-asserted-by":"crossref","unstructured":"Richardson CM, Davis KL, Ruiz-Gonz\u00e1lez C, Guimond JA, Michael HA, Paldor A, ... Paytan A (2024) The impacts of climate change on coastal groundwater. Nat Rev Earth Environ\u00a05(2):100\u2013119","DOI":"10.1038\/s43017-023-00500-2"},{"key":"1856_CR41","doi-asserted-by":"publisher","unstructured":"Rivera-Ruiz MA, L\u00f3pez-Romero JM, Mendez-Vazquez A (2024) Prediction of physical realizations of the coordinated universal time with gated recurrent unit. Rev Sci Instr 95(1):015113. https:\/\/doi.org\/10.1063\/5.0172297","DOI":"10.1063\/5.0172297"},{"issue":"6088","key":"1856_CR42","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536","journal-title":"Nature"},{"key":"1856_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.gsd.2024.101174","volume":"25","author":"S Samani","year":"2024","unstructured":"Samani S (2024) Unraveling aquifer dynamics: Time series evaluation for informed groundwater management. Groundwater for Sustainable Development 25:101174","journal-title":"Groundwater for Sustainable Development"},{"issue":"3","key":"1856_CR44","first-page":"380","volume":"22","author":"S Samani","year":"2013","unstructured":"Samani S, Boustani F, Hojati MH (2013) Screen for heavy metals from groundwater samples from industrialized zones in Marvdasht, Kharameh and Zarghan plains, Shiraz, Iran. World Applied Sciences Journal 22(3):380\u2013388","journal-title":"World Applied Sciences Journal"},{"issue":"10","key":"1856_CR45","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1007\/s11269-022-03217-x","volume":"36","author":"S Samani","year":"2022","unstructured":"Samani S, Vadiati M, Azizi F, Zamani E, Kisi O (2022) Groundwater level simulation using soft computing methods with emphasis on major meteorological components. Water Resour Manage 36(10):3627\u20133647","journal-title":"Water Resour Manage"},{"issue":"4","key":"1856_CR46","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1007\/s42979-024-02686-8","volume":"5","author":"SG Sanu","year":"2024","unstructured":"Sanu SG, Math MM (2024) Machine learning-based water management strategies for sustainable groundwater resources. SN Computer Science 5(4):338","journal-title":"SN Computer Science"},{"issue":"8","key":"1856_CR47","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"J Schmidhuber","year":"1997","unstructured":"Schmidhuber J, Hochreiter S (1997) Long short-term memory. Neural Computation 9(8):1735\u20131780","journal-title":"Neural Computation"},{"issue":"8","key":"1856_CR48","doi-asserted-by":"publisher","first-page":"e2024EF004737","DOI":"10.1029\/2024EF004737","volume":"12","author":"SL Seibert","year":"2024","unstructured":"Seibert SL, Greskowiak J, Oude Essink GH, Massmann G (2024) Understanding climate change and anthropogenic impacts on the salinization of low-lying coastal groundwater systems. Earth\u2019s Future 12(8):e2024EF004737","journal-title":"Earth's Future"},{"key":"1856_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.coastaleng.2024.104504","volume":"190","author":"A Shahabi","year":"2024","unstructured":"Shahabi A, Tahvildari N (2024) A deep-learning model for rapid spatiotemporal prediction of coastal water levels. Coastal Engineering 190:104504","journal-title":"Coastal Engineering"},{"issue":"3","key":"1856_CR50","doi-asserted-by":"publisher","first-page":"64","DOI":"10.3390\/hydrology7030064","volume":"7","author":"MJ Shin","year":"2020","unstructured":"Shin MJ, Moon SH, Kang KG, Moon DC, Koh HJ (2020) Analysis of groundwater level variations caused by the changes in groundwater withdrawals using long short-term memory network. Hydrology 7(3):64","journal-title":"Hydrology"},{"key":"1856_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111469","volume":"155","author":"T Srivastava","year":"2024","unstructured":"Srivastava T, Mullick I, Bedi J (2024) Association mining based deep learning approach for financial time-series forecasting. Applied Soft Computing 155:111469","journal-title":"Applied Soft Computing"},{"issue":"4","key":"1856_CR52","doi-asserted-by":"publisher","first-page":"bbac66","DOI":"10.1093\/bib\/bbac266","volume":"23","author":"F Sun","year":"2022","unstructured":"Sun F, Sun J, Zhao Q (2022) A deep learning method for predicting metabolite\u2013disease associations via graph neural network. Briefings in Bioinformatics 23(4):bbac66","journal-title":"Briefings in Bioinformatics"},{"key":"1856_CR53","first-page":"1","volume":"2022","author":"H Sun","year":"2022","unstructured":"Sun H, Sun J, Zhao K, Wang L, Wang K (2022b) Data-driven ICA-Bi-LSTM-combined lithium battery SOH estimation. Mathematical Problems in Engineering 2022:1\u20138","journal-title":"Mathematical Problems in Engineering"},{"issue":"2","key":"1856_CR54","doi-asserted-by":"publisher","first-page":"2866","DOI":"10.1007\/s11356-022-22375-4","volume":"30","author":"M Ta\u015fan","year":"2023","unstructured":"Ta\u015fan M, Ta\u015fan S, Demir Y (2023) Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods. Environ Sci Pollut Res 30(2):2866\u20132890","journal-title":"Environ Sci Pollut Res"},{"issue":"2","key":"1856_CR55","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1111\/gwat.13323","volume":"62","author":"L Thissen","year":"2024","unstructured":"Thissen L, Greskowiak J, Massmann G (2024) Estimating freshwater lens volume based on island circularity. Groundwater 62(2):250\u2013259","journal-title":"Groundwater"},{"issue":"1","key":"1856_CR56","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.1038\/s41598-024-52261-7","volume":"14","author":"B Tu","year":"2024","unstructured":"Tu B, Bai K, Zhan C, Zhang W (2024) Real-time prediction of ROP based on GRU-Informer. Scientific Reports 14(1):2133","journal-title":"Scientific Reports"},{"key":"1856_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2024.130637","volume":"629","author":"Z Wang","year":"2024","unstructured":"Wang Z, Wang Q, Liu Z, Wu T (2024) A deep learning interpretable model for river dissolved oxygen multi-step and interval prediction based on multi-source data fusion. J Hydrol 629:130637","journal-title":"J Hydrol"},{"key":"1856_CR58","doi-asserted-by":"publisher","first-page":"122333","DOI":"10.1016\/j.eswa.2023.122333","volume":"238","author":"H Yadav","year":"2024","unstructured":"Yadav H, Thakkar A (2024) NOA-LSTM: An efficient LSTM cell architecture for time series forecasting. Expert Systems with Applications, 238, 122333.). NOA-LSTM: An efficient LSTM cell architecture for time series forecasting. Expert Systems with Applications 238:122333","journal-title":"Expert Systems with Applications"},{"key":"1856_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2023.116618","volume":"234","author":"H Yang","year":"2023","unstructured":"Yang H, Yang T, Yang F, Yang X (2023) Assessment of groundwater salinization impact in coastal aquifers based on the shared socioeconomic pathways: An integrated modeling approach. Environ Res 234:116618","journal-title":"Environ Res"},{"issue":"1","key":"1856_CR60","doi-asserted-by":"publisher","first-page":"e2023EF003581","DOI":"10.1029\/2023EF003581","volume":"12","author":"D Zamrsky","year":"2024","unstructured":"Zamrsky D, Oude Essink GH, Bierkens MF (2024) Global impact of sea level rise on coastal fresh groundwater resources. Earth\u2019s Future 12(1):e2023EF003581","journal-title":"Earth's Future"},{"issue":"3","key":"1856_CR61","doi-asserted-by":"publisher","first-page":"553","DOI":"10.3390\/rs15030553","volume":"15","author":"Z Zang","year":"2023","unstructured":"Zang Z, Bao X, Li Y, Qu Y, Niu D, Liu N, Chen X (2023) A modified rnn-based deep learning method for prediction of atmospheric visibility. Remote Sensing 15(3):553","journal-title":"Remote Sensing"},{"issue":"1","key":"1856_CR62","doi-asserted-by":"publisher","first-page":"e2021GL095823","DOI":"10.1029\/2021GL095823","volume":"49","author":"C Zhan","year":"2022","unstructured":"Zhan C, Dai Z, Soltanian MR, Zhang X (2022) Stage-wise stochastic deep learning inversion framework for subsurface sedimentary structure identification. Geophysical Research Letters 49(1):e2021GL095823","journal-title":"Geophysical Research Letters"},{"key":"1856_CR63","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/j.jhydrol.2018.04.065","volume":"561","author":"J Zhang","year":"2018","unstructured":"Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918\u2013929","journal-title":"J Hydrol"},{"key":"1856_CR64","first-page":"147509022311578","volume":"237","author":"M Zhang","year":"2023","unstructured":"Zhang M, Taimuri G, Zhang J, Hirdaris S (2023) A deep learning method for the prediction of 6-DoF ship motions in real conditions. Proc Inst Mech Eng, Part M: J Eng Maritime Environ 237:14750902231157852","journal-title":"Proc Inst Mech Eng, Part M: J Eng Maritime Environ"},{"issue":"1","key":"1856_CR65","doi-asserted-by":"publisher","first-page":"83","DOI":"10.5194\/hess-27-83-2023","volume":"27","author":"X Zhang","year":"2023","unstructured":"Zhang X, Dong F, Chen G, Dai Z (2023b) Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks. Hydrol Earth Syst Sci 27(1):83\u201396","journal-title":"Hydrol Earth Syst Sci"},{"issue":"7","key":"1856_CR66","doi-asserted-by":"publisher","first-page":"5333","DOI":"10.1109\/TWC.2021.3139384","volume":"21","author":"T Zhou","year":"2022","unstructured":"Zhou T, Zhang H, Ai B, Xue C, Liu L (2022) Deep-learning-based spatial\u2013temporal channel prediction for smart high-speed railway communication networks. IEEE Trans Wireless Commun 21(7):5333\u20135345","journal-title":"IEEE Trans Wireless Commun"},{"key":"1856_CR67","doi-asserted-by":"publisher","first-page":"111481","DOI":"10.1016\/j.knosys.2024.111481","volume":"289","author":"K Zhou","year":"2024","unstructured":"Zhou K, Oh SK, Pedrycz W, Qiu J, Seo K (2024) A self-organizing deep network architecture designed based on LSTM network via elitism-driven roulette-wheel selection for time-series forecasting. Knowledge-Based Systems 289:111481","journal-title":"Knowledge-Based Systems"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01856-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-025-01856-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-025-01856-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T15:24:43Z","timestamp":1757172283000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-025-01856-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,3]]},"references-count":67,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1856"],"URL":"https:\/\/doi.org\/10.1007\/s12145-025-01856-3","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,3]]},"assertion":[{"value":"26 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"It is not applicable because this article contains no studies with human or animal subjects.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The undersigned gives us consent to publish identifiable details in the journal, including text, figures, and tables.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"389"}}