{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T08:06:05Z","timestamp":1779696365861,"version":"3.53.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100006138","name":"Office of Environmental Management","doi-asserted-by":"publisher","award":["DE-EM0005213"],"award-info":[{"award-number":["DE-EM0005213"]}],"id":[{"id":"10.13039\/100006138","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00521-026-11912-8","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T06:44:35Z","timestamp":1774334675000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-driven anomaly detection for water seepage monitoring using electrical resistivity tomography"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8019-5376","authenticated-orcid":false,"given":"Aris","family":"Duani Rojas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayesh","family":"Soni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Himanshu","family":"Upadhyay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonel","family":"Lagos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Danielson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hansell","family":"Gonzalez-Raymat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"11912_CR1","unstructured":"Interstate Technology & Regulatory Council (ITRC) (2020) Monitoring and maintenance objectives and approaches. https:\/\/sd-1.itrcweb.org\/ 7-monitoring-and-maintenance-objectives-and-approaches. Accessed June 2025"},{"key":"11912_CR2","unstructured":"New Jersey Department of Environmental Protection (NJDEP) (2021) Technical guidance on the capping of remediation sites. Technical report, NJDEP site remediation program. Accessed June 2025"},{"key":"11912_CR3","unstructured":"Michael RN (2023) The observer effect and the limitations of lysimeters for evaluating landfill phytocap performance. In: Proceedings of the 19th International Sardinia Symposium on Waste Management. Accessed June 2025"},{"key":"11912_CR4","unstructured":"Agency USEP Geophysical methods. https:\/\/clu-in.org\/characterization\/ technologies\/default2.focus\/sec\/Geophysical%20Methods\/cat\/Electrical Resistivity Tomography\/n.d.; Accessed June 2025"},{"key":"11912_CR5","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1016\/j.scitotenv.2018.08.179","volume":"648","author":"L Busato","year":"2019","unstructured":"Busato L, Boaga J, Perri MT, Majone B, Bellin A, Cassiani G (2019) Hydrogeo- physical characterization and monitoring of the hyporheic and riparian zones: the vermigliana creek case study. Sci Total Environ 648:1105\u20131120. https:\/\/doi.org\/10.1016\/j.scitotenv.2018.08.179","journal-title":"Sci Total Environ"},{"issue":"3","key":"11912_CR6","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1190\/1.2907156","volume":"73","author":"CR Miller","year":"2008","unstructured":"Miller CR, Routh PS, Brosten TR, McNamara JP (2008) Application of time-lapse ert imaging to watershed characterization. Geophysics 73(3):7\u201317. https:\/\/doi.org\/10.1190\/1.2907156","journal-title":"Geophysics"},{"issue":"1","key":"11912_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/2014RG000465","volume":"53","author":"AD Parsekian","year":"2015","unstructured":"Parsekian AD, Singha K, Minsley BJ, Holbrook WS, Slater L (2015) Multiscale geophysical imaging of the critical zone. Rev Geophys 53(1):1\u201326","journal-title":"Rev Geophys"},{"issue":"6","key":"11912_CR8","doi-asserted-by":"publisher","first-page":"2257","DOI":"10.1007\/s00024-021-02746-7","volume":"178","author":"A Petit","year":"2021","unstructured":"Petit A, Cerepi A, Le Roux O, Loisy C, Kennedy S, Estublier A, Noirez S, Garcia B, El khamlichi A (2021) Study of water transfer dynamics in a carbonate vadose zone from geophysical properties. Pure Appl Geophys 178(6):2257\u20132285","journal-title":"Pure Appl Geophys"},{"key":"11912_CR9","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.geomorph.2015.10.027","volume":"253","author":"S Uhlemann","year":"2016","unstructured":"Uhlemann S, Smith A, Chambers J, Dixon N, Dijkstra T, Haslam E, Meldrum P, Merritt A, Gunn D, Mackay J (2016) Assessment of ground-based monitoring techniques applied to landslide investigations. Geomorphology 253:438\u2013451. https:\/\/doi.org\/10.1016\/j.geomorph.2015.10.027","journal-title":"Geomorphology"},{"key":"11912_CR10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-642-17435-3_6","volume-title":"Hydrogeological and environmental investigations in karst systems","author":"SD Carriere","year":"2015","unstructured":"Carriere SD, Chalikakis K, Danquigny C, Clement R, Emblanch C (2015) Feasibility and limits of electrical resistivity tomography to monitor water infiltration through karst medium during a rainy event. Hydrogeological and environmental investigations in karst systems. Springer, Berlin Heidelberg, pp 45\u201355"},{"issue":"02","key":"11912_CR11","doi-asserted-by":"publisher","first-page":"107","DOI":"10.2118\/1863-A","volume":"8","author":"MH Waxman","year":"1968","unstructured":"Waxman MH, Smits LJM (1968) Electrical conductivities in oil-bearing shaly sands. Soc Pet Eng J 8(02):107\u2013122. https:\/\/doi.org\/10.2118\/1863-A","journal-title":"Soc Pet Eng J"},{"key":"11912_CR12","doi-asserted-by":"publisher","DOI":"10.1029\/2007GL031124","author":"K Hayley","year":"2007","unstructured":"Hayley K, Bentley LR, Gharibi M, Nightingale M (2007) Low temperature dependence of electrical resistivity: implications for near surface geophysical monitoring. Geophys Res Lett. https:\/\/doi.org\/10.1029\/2007GL031124","journal-title":"Geophys Res Lett"},{"issue":"1","key":"11912_CR13","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s11119-009-9156-7","volume":"12","author":"R Ma","year":"2011","unstructured":"Ma R, McBratney A, Whelan B, Minasny B, Short M (2011) Comparing temperature correction models for soil electrical conductivity measurement. Precis Agric 12(1):55\u201366","journal-title":"Precis Agric"},{"key":"11912_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/geosciences9040167","author":"C Comina","year":"2019","unstructured":"Comina C, Giordano N, Ghidone G, Fischanger F (2019) Time-lapse 3D electric tomography for short-time monitoring of an experimental heat storage system. Geosciences. https:\/\/doi.org\/10.3390\/geosciences9040167","journal-title":"Geosciences"},{"issue":"6","key":"11912_CR15","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1190\/geo2017-0024.1","volume":"82","author":"N Lesparre","year":"2017","unstructured":"Lesparre N, Nguyen F, Kemna A, Robert T, Hermans T, Daoudi M, Flores-Orozco A (2017) A new approach for time-lapse data weighting in electrical resistivity tomography. Geophysics 82(6):325\u2013333","journal-title":"Geophysics"},{"issue":"12","key":"11912_CR16","doi-asserted-by":"publisher","first-page":"487","DOI":"10.3390\/geosciences9120487","volume":"9","author":"A Nivorlis","year":"2019","unstructured":"Nivorlis A, Dahlin T, Rossi M, Hoglund N, Sparrenbom C (2019) Multidisciplinary characterization of chlorinated solvents contamination and in-situ remediation with the use of the direct current resistivity and time-domain induced polarization tomography. Geosciences 9(12):487","journal-title":"Geosciences"},{"key":"11912_CR17","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.jappgeo.2016.10.037","volume":"136","author":"P Tildy","year":"2017","unstructured":"Tildy P, Neducza B, Nagy P, Kanli AI, Hegymegi C (2017) Time lapse 3D geoelectric measurements for monitoring of in-situ remediation. J Appl Geophys 136:99\u2013113. https:\/\/doi.org\/10.1016\/j.jappgeo.2016.10.037","journal-title":"J Appl Geophys"},{"key":"11912_CR18","doi-asserted-by":"publisher","first-page":"9866","DOI":"10.1038\/s41598-020-66516-6","volume":"10","author":"AR Costall","year":"2020","unstructured":"Costall AR, Harris BD, Teo B, Schaa R, Wagner FM, Pigois JP (2020) Groundwater throughflow and seawater intrusion in high quality coastal aquifers. Sci Rep 10:9866","journal-title":"Sci Rep"},{"issue":"7","key":"11912_CR19","doi-asserted-by":"publisher","first-page":"107","DOI":"10.3390\/electronics7070107","volume":"7","author":"T-T Chen","year":"2018","unstructured":"Chen T-T, Hung Y-C, Hsueh M-W, Yeh Y-H, Weng K-W (2018) Evaluating the application of electrical resistivity tomography for investigating seawater intrusion. Electronics 7(7):107","journal-title":"Electronics"},{"key":"11912_CR20","doi-asserted-by":"publisher","unstructured":"Ru H, Chen J, Sun Z, Tai S, Zhao W, Kumar R (2024) A wood anomaly detection system based on electrical resistivity tomography and tiny machine learning. In: 2024 control instrumentation system conference (CISCON) 1\u20135. https:\/\/doi.org\/10.1109\/CISCON62171.2024.10696520","DOI":"10.1109\/CISCON62171.2024.10696520"},{"key":"11912_CR21","doi-asserted-by":"publisher","unstructured":"Pebriyanto Y, Dahlan K, Sari Y (2017) Electrical resistivity tomography using wenner \u03b2-schlumberger configuration for anomaly detection in the soil. In: IOP conference series: earth and environmental science 58, 012012 https:\/\/doi.org\/10.1088\/1755-1315\/58\/1\/012012","DOI":"10.1088\/1755-1315\/58\/1\/012012"},{"key":"11912_CR22","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/ad7d2e","author":"G Chen","year":"2024","unstructured":"Chen G, Santosa F, Titi A (2024) Determination of a small elliptical anomaly in electrical impedance tomography using minimal measurements. Inverse Probl. https:\/\/doi.org\/10.1088\/1361-6420\/ad7d2e","journal-title":"Inverse Probl"},{"issue":"2","key":"11912_CR23","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1093\/gji\/ggab023","volume":"225","author":"K Sabor","year":"2021","unstructured":"Sabor K, Jougnot D, Guerin R, Steck B, Henault J-M, Apffel L, Vautrin D (2021) A data mining approach for improved interpretation of ert inverted sections using the dbscan clustering algorithm. Geophys J Int 225(2):1304\u20131318","journal-title":"Geophys J Int"},{"issue":"1","key":"11912_CR24","doi-asserted-by":"publisher","first-page":"2621","DOI":"10.1038\/s41598-025-86747-9","volume":"15","author":"L Du","year":"2025","unstructured":"Du L, Zhu W, Wang L, Li H, Jiao X, Qin T (2025) Using roof borehole electrical resistivity tomography to monitor roof water infiltration in a mine work face. Sci Rep 15(1):2621","journal-title":"Sci Rep"},{"key":"11912_CR25","doi-asserted-by":"crossref","unstructured":"Ma Y, Ji X, BenHassan NM, Luo Y (2018) A deep-learning method for automatic fault detection. In: SEG technical program expanded abstracts 2018. Society of exploration geophysicists","DOI":"10.1190\/segam2018-2984932.1"},{"key":"11912_CR26","doi-asserted-by":"crossref","unstructured":"Guillen-Rondon P, Cobos C, Larrazabal G, Diz A (2019) Machine learning: a deep learning approach for seismic structural evaluation. In: SEG technical program expanded abstracts 2019. Society of exploration geophysicists","DOI":"10.1190\/segam2019-3216712.1"},{"key":"11912_CR27","doi-asserted-by":"publisher","first-page":"130458","DOI":"10.1016\/j.jhydrol.2023.130458","volume":"628","author":"KP Tripathy","year":"2024","unstructured":"Tripathy KP, Mishra AK (2024) Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions. J Hydrol (Amst) 628:130458","journal-title":"J Hydrol (Amst)"},{"issue":"7743","key":"11912_CR28","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","volume":"566","author":"M Reichstein","year":"2019","unstructured":"Reichstein M, Camps-Valls G, Stevens B et al (2019) Deep learning and process understanding for data-driven earth system science. Nature 566(7743):195\u2013204","journal-title":"Nature"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11912-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-11912-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11912-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T07:39:39Z","timestamp":1779694779000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-11912-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":28,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["11912"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-11912-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]},"assertion":[{"value":"30 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2026","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"207"}}