[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122758-en":3,"doc-seo-122758-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122758,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A machine learning-based approach for mapping leachate contamination using geoelectrical methods - read online free","Leachate is a principal pollutant in landfills, and its harmful effects may persist long after closure. Geoelectrical imaging can visualize the leachate plume, yet conventional inversion yields separate physical parameters that create interpretive ambiguities and uncertainty in defining contaminated zones. This work introduces a machine learning framework integrating geoelectrical tomographic data. Using multivariate analysis of electrical resistivity, chargeability and normalized chargeability for two urban sites, K-means clustering updates cross-sections and supports clearer, less ambiguous identification of leachate accumulation zones. Results are corroborated by borehole data for one site, showing the promise of combining geophysical imaging with unsupervised learning for pollution assessment.","Waste Management 157 (2023) 121–129  \nContents lists available at ScienceDirect  \nWaste Management  \njournal [homepage: www.elsevier.com/locate/wasman](homepage: www.elsevier.com/locate/wasman)  \n| A machine learning-based approach for mapping leachate contamination using geoelectrical methods\u003Cbr>Ester Piegaria, *, Giorgio De Donno b, Davide Melegarib, Valeria Paolettia\u003Cbr>a Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse, Universit`a degli Studi di Napoli Federico II, Naples, Italy b Dipartimento di Ingegneria Civile Edile e Ambientale, “Sapienza” Universit`a di Roma, Rome, Italy |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Leachate contamination detection Machine learning\u003Cbr>K-means clustering geophysical imaging Electrical resistivity tomography Induced polarization tomography |  | Leachate is the main source of pollution in landfills and its negative impacts continue for several years even after landfill closure. In recent years, geophysical methods are recognized as effective tools for providing an imaging of the leachate plume. However, they produce subsurface cross-sections in terms of individual physical quantities, leaving room for ambiguities on interpretation of geophysical models and uncertainties in the definition of contaminated zones. In this work, we propose a machine learning-based approach for mapping leachate contamination through an effective integration of geoelectrical tomographic data. We apply the proposed approach for the characterization of two urban landfills. For both cases, we perform a multivariate analysis on datasets consisting of electrical resistivity, chargeability and normalized chargeability (chargeability-to-resistivity ratio) data extracted from previously inverted model sections. By executing a K-Means cluster analysis, we find that the best partition of the two datasets contains ten and eleven classes, respectively. From such classes and also introducing a distance-based colour code, we get updated cross-sections and provide an easy and less ambiguous identification of the leachate accumulation zones. The latter turn out to be characterized by coordinate values of cluster centroids\u003C3 Ωm and >27 mV/V and 11 mS/m. Our findings, also supported by borehole data for one of the investigation sites, show that the combined use of geophysical imaging and unsupervised machine learning is promising and can yield new perspectives for the characterization of leachate distribution and pollution assessment in landfills. |  |\n\n1. Introduction  \nAlthough there is an increased awareness on the importance of environment protection, urban waste management is one the most important environmental issues. In many countries there is still a little use of recycling or reuse actions, and the majority of municipal solid waste is destined for landfills (WHO, 2015). Waste decomposition generates leachate that is a highly contaminated liquid consisting of a mixture from organic degradation products, liquid waste and rainwater. Leachate infiltration causes serious environmental issues to groundwater and soils and, therefore, identifying and monitoring its flow pathways has major implications on designing a risk mitigation strategy (Mukherjee et al., 2015; Lavagnolo, 2019; Vaccari et al., 2019; Morita et al., 2021; Ergene et al., 2022). To this aim, geophysical methods often represent the only cost-effective, rapid and non-invasive choice for mapping large areas, such as those encountered in urban landfills, down to tens of meters (e.g., Di Maio et al., 2018).  \nIn last decades, many studies have demonstrated that geoelectrical methods can be effective in identifying landfill leachate (e.g., Soupioset al., 2007; Abdulrahman et al., 2016; Bichet et al., 2016; Raji and Adeoye, 2017; Flores-Orozco et al., 2020; Zaini et al., 2022). The geoelectrical devices work through injection in the ground of a directcurrent. The measurements of the resulting","cbCaikvvYTo8BupH","https://ap.wps.com/l/cbCaikvvYTo8BupH","pdf",9501164,1,9,"English","en",105,"# Introduction\n## Leachate as a key pollutant and monitoring challenges\n## Geoelectrical methods for landfill leachate characterization\n## Ambiguities in interpreting inverted geophysical models\n## Normalized chargeability and its links to contamination","[{\"question\":\"Why is mapping leachate contamination important for landfills?\",\"answer\":\"Leachate is highly contaminated and can infiltrate groundwater and soils. Identifying and monitoring its flow pathways is essential for designing effective risk mitigation strategies.\"},{\"question\":\"What limitation affects geoelectrical imaging when defining contaminated zones?\",\"answer\":\"Inverted geophysical models often output subsurface cross-sections as separate physical quantities, which can lead to ambiguity in interpreting contaminated zones, especially with clayey soils.\"},{\"question\":\"How does the proposed method use machine learning to improve interpretation?\",\"answer\":\"The approach integrates geoelectrical tomographic data and applies multivariate analysis with K-means clustering on resistivity, chargeability, and normalized chargeability-derived information. The resulting classes and a distance-based color scheme produce updated cross-sections that help identify leachate accumulation zones with less ambiguity.\"}]","A machine learning-based approach for mapping leachate contamination using geoelectrical methods - read online free | PDF",1785812748,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-based-approach-for-mapping-leachate-contamination-using-geoelectrical-methods-read-online-free","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-based-approach-for-mapping-leachate-contamination-using-geoelectrical-methods-read-online-free/122758/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is mapping leachate contamination important for landfills?","Question",{"text":75,"@type":76},"Leachate is highly contaminated and can infiltrate groundwater and soils. Identifying and monitoring its flow pathways is essential for designing effective risk mitigation strategies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation affects geoelectrical imaging when defining contaminated zones?",{"text":80,"@type":76},"Inverted geophysical models often output subsurface cross-sections as separate physical quantities, which can lead to ambiguity in interpreting contaminated zones, especially with clayey soils.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed method use machine learning to improve interpretation?",{"text":84,"@type":76},"The approach integrates geoelectrical tomographic data and applies multivariate analysis with K-means clustering on resistivity, chargeability, and normalized chargeability-derived information. The resulting classes and a distance-based color scheme produce updated cross-sections that help identify leachate accumulation zones with less ambiguity.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]