[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122718-en":3,"doc-seo-122718-105":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":20,"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},122718,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Predicting bacterial transport through saturated porous media using an automated machine learning model","Escherichia coli can migrate from manure-amended soil to groundwater during rainfall or irrigation, so accurate prediction of vertical transport under saturated conditions supports mitigation of microbiological contamination risk. This study compiles 377 datasets from 61 publications and trains six machine-learning models to predict first-order attachment coefficient and spatial removal rate from eight environmental and medium variables. Higher retention scenarios show improved performance, and Gradient Boosting and Extreme Gradient Boosting outperform other algorithms. Key variable importance is dominated by pore water velocity, ionic strength, median grain size, and column length.","TYPE Original Research PUBLISHED 10 May 2023  \nDOI 10.3389/fmicb.2023.1152059  \nOPEN ACCESS  \nEDITED BY  \nMuhammad Shaaban,  \nBahauddin Zakariya University, Pakistan  \nREVIEWED BY  \nAlex Furman,  \nTechnion Israel Institute of Technology, Israel Murray Close,  \nInstitute of Environmental Science and Research (ESR), New Zealand  \n*CORRESPONDENCE  \nXijuan Chen  \n [chenxj@iae.ac.cn](chenxj@iae.ac.cn)  \nRECEIVED 27 January 2023  \nACCEPTED 25 April 2023  \nPUBLISHED 10 May 2023  \nCITATION  \nChen F, Zhou B, Yang L, Chen X and Zhuang J (2023) Predicting bacterial transport through saturated porous media using an automated machine learning model.  \nFront. Microbiol. 14:1152059.  \ndoi: 10.3389/fmicb.2023.1152059  \nCOPYRIGHT  \n© 2023 Chen, Zhou, Yang, Chen and Zhuang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredicting bacterial transport through saturated porous media using an automated machine learning model  \nFengxian Chen 1, Bin Zhou 2, Liqiong Yang 1, Xijuan Chen 1* and Jie Zhuang3  \n1 Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China, 2 Faculty of Medicine, University of Augsburg, Augsburg, Germany, 3 Department of Biosystems Engineering and Soil Science, Center for Environmental Biotechnology, The University of Tennessee, Knoxville, TN, United States  \nEscherichia coli, as an indicator of fecal contamination, can move from manureamended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants’ transport in the environment.  \nKEYWORDS  \nbacterial transport, automated machine learning, first-order attachment coefficient, spatial removal rate, machine learning  \nHighlights  \n• The predictive models showed better performance when bacterial retention was high.  \n• Spatial removal rate is a better target variable than first-order attachment coefficient.  \n• Algorithms based on gradient boosting outperformed other machine learning algorithms.  \nFrontiers in Microb","cbCaiiczGnyTRMwe","https://ap.wps.com/l/cbCaiiczGnyTRMwe","pdf",7516935,1,10,"English","en",105,"# Introduction\n## Background and problem significance\n## Existing experimental and modeling approaches\n# Methods\n## Dataset compilation\n## Input and target variables\n## Machine learning model training\n# Results\n## Model performance and retention scenarios\n## Algorithm comparison\n## Feature importance\n# Discussion\n## Environmental risk evaluation value\n## Generalization to other contaminants","[{\"question\":\"What endpoints does the automated machine learning model predict for E. coli transport?\",\"answer\":\"It predicts the first-order attachment coefficient and the spatial removal rate as target variables.\"},{\"question\":\"Which input variables are used to drive the predictions?\",\"answer\":\"Eight variables are used, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content.\"},{\"question\":\"Which machine learning algorithms performed best and what variables were most important?\",\"answer\":\"Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. Most models found pore water velocity, ionic strength, median grain size, and column length to have higher importance.\"}]","Predicting bacterial transport through saturated porous media using an automated machine learning model | PDF",1785812505,25,{"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},"predicting-bacterial-transport-through-saturated-porous-media-using-an-automated-machine-learning-model","",{"@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/predicting-bacterial-transport-through-saturated-porous-media-using-an-automated-machine-learning-model/122718/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What endpoints does the automated machine learning model predict for E. coli transport?","Question",{"text":75,"@type":76},"It predicts the first-order attachment coefficient and the spatial removal rate as target variables.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which input variables are used to drive the predictions?",{"text":80,"@type":76},"Eight variables are used, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms performed best and what variables were most important?",{"text":84,"@type":76},"Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. Most models found pore water velocity, ionic strength, median grain size, and column length to have higher importance.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]