{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T22:15:25Z","timestamp":1787782525678,"version":"build-2784847793"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003195","name":"Ministerie van Economische Zaken","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003195","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001831","name":"Technische Universiteit Delft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001831","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003246","name":"Dutch Research Council","doi-asserted-by":"publisher","award":["ALWET.2019.003"],"award-info":[{"award-number":["ALWET.2019.003"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100022114","name":"Wetsus","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100022114","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007196","name":"Ministerie van Infrastructuur en Milieu","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007196","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Contaminant Hydrology"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1016\/j.jconhyd.2025.104771","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T16:17:13Z","timestamp":1762186633000},"page":"104771","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Machine learning-based in-situ detection of toxic petroleum hydrocarbons in groundwater"],"prefix":"10.1016","volume":"276","author":[{"given":"C.L.R.","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.M.","family":"Wagterveld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L.C.","family":"Rietveld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.M.","family":"van Breukelen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.jconhyd.2025.104771_bb0005","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/technologies9030052","article-title":"Effect of data scaling methods on machine learning algorithms and model performance","volume":"9","author":"Ahsan","year":"2021","journal-title":"Technologies"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0010","unstructured":"ALS Global. (n.d.) Petrol Pack Basic: BTEX and TPH [OV-20h] in Water. Retrieved from https:\/\/www.alsglobal.no\/en\/package\/environment_1\/water_2\/combination-packages_8\/petrol-pack-basic-btex-and-tph-ov-20h-in-water_44640 (on 28 September 2025)."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0015","author":"Atlas Scientific LLC"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0020","author":"Atlas Scientific LLC"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0025","author":"Atlas Scientific LLC"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0030","author":"Atlas Scientific LLC"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0035","series-title":"A Technical Guide for Demonstrating Monitored Natural Attenuation of Petroleum Hydrocarbons in Groundwater","author":"Beck","year":"2010"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0040","series-title":"Hybrid Water Quality Prediction with Frequency Domain Conversion Enhancement and Seasonal Decomposition. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 5200\u20135205","author":"Bi","year":"2023"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0045","series-title":"Building Machine Learning and Deep Learning Models on Google Cloud Platform","article-title":"Logistic regression","author":"Bisong","year":"2019"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0050","series-title":"Measuring the Oxidation\u2013reduction Potential of Important Oxidants in Drinking Water","first-page":"E10","volume":"106","author":"Copeland","year":"2014"},{"issue":"2","key":"10.1016\/j.jconhyd.2025.104771_bb0055","doi-asserted-by":"crossref","first-page":"51","DOI":"10.3390\/bios12020051","article-title":"Paving the way for a green transition in the design of sensors and biosensors for the detection of volatile organic compounds (VOCs)","volume":"12","author":"Cova","year":"2022","journal-title":"Biosensors"},{"issue":"9","key":"10.1016\/j.jconhyd.2025.104771_bb0060","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ab1eeb","article-title":"Measuring imperfections of water quality sensors in water distribution networks","volume":"30","author":"de Winter","year":"2019","journal-title":"Meas. Sci. Technol."},{"issue":"8","key":"10.1016\/j.jconhyd.2025.104771_bb0065","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TSMC.2017.2664143","article-title":"Regularizing multilayer perceptron for robustness","volume":"48","author":"Dey","year":"2018","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2021.113359","article-title":"A machine learning approach for prioritizing groundwater testing for per-and polyfluoroalkyl substances (PFAS)","volume":"295","author":"George","year":"2021","journal-title":"J. Environ. Manage."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2021.111031","article-title":"Phytotoxicity of petroleum hydrocarbons: sources, impacts and remediation strategies","volume":"197","author":"Haider","year":"2021","journal-title":"Environ. Res."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0080","article-title":"XGBoost to enhance learner performance prediction","volume":"7","author":"Hakkal","year":"2024","journal-title":"Comput. Educ."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0085","series-title":"Noise in Datasets: What Are the Impacts on Classification Performance? 11th International Conference on Pattern Recognition Applications and Methods","author":"Hasan","year":"2022"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0090","unstructured":"Irvine, T. (n.d.). Shock and Vibration Response Spectra Course Unit 2A. Sine Vibration Characteristics."},{"issue":"1","key":"10.1016\/j.jconhyd.2025.104771_bb0095","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/21553769.2014.933716","article-title":"Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas","volume":"8","author":"Khatri","year":"2015","journal-title":"Front. Life Sci."},{"issue":"7","key":"10.1016\/j.jconhyd.2025.104771_bb0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2020.100115","article-title":"Statistical hypothesis testing versus machine learning binary classification: distinctions and guidelines","volume":"1","author":"Li","year":"2020","journal-title":"Patterns"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0105","doi-asserted-by":"crossref","DOI":"10.4236\/jwarp.2017.913104","article-title":"Colorado water watch: real-time groundwater monitoring for possible contamination from oil and gas activities","volume":"9","author":"Li","year":"2017","journal-title":"J. Water Resour. Protect."},{"issue":"1","key":"10.1016\/j.jconhyd.2025.104771_bb0110","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00244-020-00805-z","article-title":"Sources and consequences of groundwater contamination","volume":"80","author":"Li","year":"2021","journal-title":"Arch. Environ. Contam. Toxicol."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2022.118078","article-title":"Interpretable tree-based ensemble model for predicting beach water quality","volume":"211","author":"Li","year":"2022","journal-title":"Water Res."},{"issue":"4","key":"10.1016\/j.jconhyd.2025.104771_bb0120","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1007\/s11440-021-01440-1","article-title":"Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction","volume":"17","author":"Lin","year":"2022","journal-title":"Acta Geotech."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2022.161199","article-title":"Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: a comparison with conventional methods","volume":"864","author":"Lyons","year":"2023","journal-title":"Sci. Total Environ."},{"issue":"9","key":"10.1016\/j.jconhyd.2025.104771_bb0130","doi-asserted-by":"crossref","first-page":"1126","DOI":"10.1016\/j.ecoleng.2010.01.004","article-title":"An integrated model for assessing the risk of TCE groundwater contamination to human receptors and surface water ecosystems","volume":"36","author":"McKnight","year":"2010","journal-title":"Ecol. Eng."},{"issue":"1","key":"10.1016\/j.jconhyd.2025.104771_bb0135","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10462-021-10033-z","article-title":"A review on weight initialization strategies for neural networks","volume":"55","author":"Narkhede","year":"2022","journal-title":"Artif. Intell. Rev."},{"issue":"6","key":"10.1016\/j.jconhyd.2025.104771_bb0140","doi-asserted-by":"crossref","first-page":"4156","DOI":"10.1002\/2015WR016964","article-title":"Reactive transport modeling of geochemical controls on secondary water quality impacts at a crude oil spill site near Bemidji, MN","volume":"51","author":"Ng","year":"2015","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2025.123535","article-title":"The spatiotemporal evolution of dissolved-phase NAPL plumes revealed by the integrated groundwater quality and machine learning models","volume":"280","author":"Qiao","year":"2025","journal-title":"Water Res."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0150","unstructured":"Rynkiewicz, J. (2019). On overfitting of multilayer perceptrons for classification. In J. Rynkiewicz (ed.), Computational Intelligence and Machine Learning. http:\/\/www.i6doc.com\/en\/."},{"issue":"13","key":"10.1016\/j.jconhyd.2025.104771_bb0155","doi-asserted-by":"crossref","first-page":"7418","DOI":"10.1021\/acs.est.8b00017","article-title":"In situ monitoring of groundwater contamination using the kalman filter","volume":"52","author":"Schmidt","year":"2018","journal-title":"Environ. Sci. Tech."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0160","article-title":"Adversarially robust generalization requires more data","volume":"31","author":"Schmidt","year":"2018","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0165","author":"Schooltink"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0170","first-page":"99","article-title":"Supervised classification algorithms in machine learning: a survey and review","volume":"937","author":"Sen","year":"2020","journal-title":"Adv. Intell. Syst. Comput."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0175","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/978-981-15-8358-2_4","article-title":"Application of artificial intelligence in predicting groundwater contaminants","author":"Singh","year":"2021","journal-title":"Water Pollut. Manag. Pract."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0180","author":"U.S. Environmental Protection Agency"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0185","unstructured":"U.S. Geological Survey. (n.d.). USGS Water Data for the Nation. U.S. Department of the Interior. Retrieved August 6, 2024, from https:\/\/waterdata.usgs.gov\/nwis."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0190","series-title":"Estimation of Dependences Based on Empirical Data (Springer Series in Statistics)","author":"Vapnik","year":"1982"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0195","first-page":"569","article-title":"Estimation of generalization error: random and fixed inputs","volume":"16","author":"Wang","year":"2006","journal-title":"Stat. Sin."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0200","series-title":"ITM Web of Conferences","article-title":"Kernel density estimation and its application","volume":"23","author":"W\u0119glarczyk","year":"2018"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0205","doi-asserted-by":"crossref","DOI":"10.1029\/2023WR036644","article-title":"Reactive transport modeling for exploring the potential of water quality sensors to estimate hydrocarbon levels in groundwater","volume":"60","author":"Wu","year":"2024","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.jconhyd.2025.104771_bb0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2022.111832","article-title":"Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning","volume":"258","author":"Xiao","year":"2022","journal-title":"Energ. Buildings"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0215","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s41664-018-0068-2","article-title":"On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning","volume":"2","author":"Xu","year":"2018","journal-title":"J. Anal. Test."},{"issue":"5","key":"10.1016\/j.jconhyd.2025.104771_bb0220","doi-asserted-by":"crossref","first-page":"763","DOI":"10.3390\/w16050763","article-title":"Groundwater hydrochemistry and recharge process impacted by human activities in an Oasis\u2013Desert in Central Asia","volume":"16","author":"Yao","year":"2024","journal-title":"Water"},{"key":"10.1016\/j.jconhyd.2025.104771_bb0225","series-title":"ITM Web of Conferences","article-title":"Enhancing distributed machine learning through data shuffling: techniques, challenges, and implications","volume":"73","author":"Zhang","year":"2025"},{"issue":"3","key":"10.1016\/j.jconhyd.2025.104771_bb0230","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s10462-004-0751-8","article-title":"Class noise vs. attribute noise: a quantitative study","volume":"22","author":"Zhu","year":"2004","journal-title":"Artif. Intell. Rev."}],"container-title":["Journal of Contaminant Hydrology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169772225002761?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169772225002761?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T13:27:52Z","timestamp":1766064472000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169772225002761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":46,"alternative-id":["S0169772225002761"],"URL":"https:\/\/doi.org\/10.1016\/j.jconhyd.2025.104771","relation":{},"ISSN":["0169-7722"],"issn-type":[{"value":"0169-7722","type":"print"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning-based in-situ detection of toxic petroleum hydrocarbons in groundwater","name":"articletitle","label":"Article Title"},{"value":"Journal of Contaminant Hydrology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jconhyd.2025.104771","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104771"}}