{"id":"https://openalex.org/W7225728328","doi":"https://doi.org/10.1061/jwrmd5.wreng-7717","title":"Effectiveness of Different Ground Truths at Detecting Lead Service Pipes Using Machine Learning","display_name":"Effectiveness of Different Ground Truths at Detecting Lead Service Pipes Using Machine Learning","publication_year":2026,"publication_date":"2026-11-10","ids":{"openalex":"https://openalex.org/W7225728328","doi":"https://doi.org/10.1061/jwrmd5.wreng-7717"},"language":"en","primary_location":{"id":"doi:10.1061/jwrmd5.wreng-7717","doi":"https://doi.org/10.1061/jwrmd5.wreng-7717","is_oa":false,"landing_page_url":"https://doi.org/10.1061/jwrmd5.wreng-7717","pdf_url":null,"source":{"id":"https://openalex.org/S127754761","display_name":"Journal of Water Resources Planning and Management","issn_l":"0733-9496","issn":["0733-9496","1943-5452"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-140","russia-white-list-1"],"host_organization":"https://openalex.org/P4310315747","host_organization_name":"American Society of Civil Engineers","host_organization_lineage":["https://openalex.org/P4310315747"],"host_organization_lineage_names":["American Society of Civil Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Water Resources Planning and Management","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008497929","display_name":"Matthew Hayslep","orcid":"https://orcid.org/0000-0002-0767-0619","observed_orcids":["https://orcid.org/0000-0002-0767-0619"]},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Matthew Hayslep","raw_affiliation_strings":["Univ. of Exeter"],"raw_orcid":"https://orcid.org/0000-0002-0767-0619","affiliations":[{"raw_affiliation_string":"Univ. of Exeter","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052558681","display_name":"Raziyeh Farmani","orcid":"https://orcid.org/0000-0001-8148-0488","observed_orcids":["https://orcid.org/0000-0001-8148-0488","https://orcid.org/0000-0002-9818-1879"]},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Raziyeh Farmani","raw_affiliation_strings":["Univ. of Exeter"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Exeter","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161386949","display_name":"Louise Newstead","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I23923803","display_name":"University of Exeter","ror":"https://ror.org/03yghzc09","country_code":"GB","type":"education","lineage":["https://openalex.org/I23923803"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Louise Newstead","raw_affiliation_strings":["Univ. of Exeter"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of Exeter","institution_ids":["https://openalex.org/I23923803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058871746","display_name":"Edward C. Keedwell","orcid":"https://orcid.org/0000-0003-3650-6487","observed_orcids":["https://orcid.org/0000-0003-3650-6487"]},"institutions":[{"id":"https://openalex.org/I4405271043","display_name":"South West Water (United Kingdom)","ror":"https://ror.org/01rhdc921","country_code":"GB","type":"company","lineage":["https://openalex.org/I4405271043"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Edward Keedwell","raw_affiliation_strings":["South West Water"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South West Water","institution_ids":["https://openalex.org/I4405271043"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5161396168","display_name":"Jason P. Harvey","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405271043","display_name":"South West Water (United Kingdom)","ror":"https://ror.org/01rhdc921","country_code":"GB","type":"company","lineage":["https://openalex.org/I4405271043"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jason P. Harvey","raw_affiliation_strings":["South West Water"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South West Water","institution_ids":["https://openalex.org/I4405271043"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"153","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C2777093003","wikidata":"https://www.wikidata.org/wiki/Q6508345","display_name":"Lead (geology)","level":2,"score":0.6599000096321106},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6043000221252441},{"id":"https://openalex.org/C2781468064","wikidata":"https://www.wikidata.org/wiki/Q1267117","display_name":"Lead time","level":2,"score":0.5593000054359436},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5358999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5304999947547913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47429999709129333},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.41179999709129333},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3716000020503998},{"id":"https://openalex.org/C76178495","wikidata":"https://www.wikidata.org/wiki/Q4808784","display_name":"Asset (computer security)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.33169999718666077},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.32580000162124634},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32420000433921814},{"id":"https://openalex.org/C533363959","wikidata":"https://www.wikidata.org/wiki/Q474883","display_name":"Water industry","level":3,"score":0.3061999976634979},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C153823671","wikidata":"https://www.wikidata.org/wiki/Q1049799","display_name":"Water resources","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2696000039577484},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1061/jwrmd5.wreng-7717","doi":"https://doi.org/10.1061/jwrmd5.wreng-7717","is_oa":false,"landing_page_url":"https://doi.org/10.1061/jwrmd5.wreng-7717","pdf_url":null,"source":{"id":"https://openalex.org/S127754761","display_name":"Journal of Water Resources Planning and Management","issn_l":"0733-9496","issn":["0733-9496","1943-5452"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","jufo-1","norway-1","poland-140","russia-white-list-1"],"host_organization":"https://openalex.org/P4310315747","host_organization_name":"American Society of Civil Engineers","host_organization_lineage":["https://openalex.org/P4310315747"],"host_organization_lineage_names":["American Society of Civil Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Water Resources Planning and Management","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1573178907","https://openalex.org/W1591261915","https://openalex.org/W1976526581","https://openalex.org/W1982359250","https://openalex.org/W2148143831","https://openalex.org/W2161613947","https://openalex.org/W2522138202","https://openalex.org/W2528923780","https://openalex.org/W2726182856","https://openalex.org/W2811411010","https://openalex.org/W2898227265","https://openalex.org/W2911964244","https://openalex.org/W2949676527","https://openalex.org/W2963596856","https://openalex.org/W3037030292","https://openalex.org/W3172699115","https://openalex.org/W3188247044","https://openalex.org/W3188266647","https://openalex.org/W3191696375","https://openalex.org/W4206492908","https://openalex.org/W4220937950","https://openalex.org/W4289751788","https://openalex.org/W4360874987","https://openalex.org/W4366850565","https://openalex.org/W4378417908","https://openalex.org/W4388922432","https://openalex.org/W4392356648","https://openalex.org/W4396773505","https://openalex.org/W4408989338","https://openalex.org/W4409647442"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Replacing":[1],"all":[2],"existing":[3,159],"lead":[4,18,99,107,195,237],"service":[5,67],"pipes":[6,68],"(SPs)":[7],"is":[8,62],"an":[9,214],"important":[10],"strategic":[11],"goal":[12,61,96],"for":[13,167],"the":[14,27,35,45,76,94,131,162,234,243],"water":[15,29,154],"industry":[16,30],"because":[17],"SPs":[19],"represent":[20],"a":[21,65,136,146,152,191,194],"clear":[22],"health":[23],"risk.":[24],"For":[25],"example,":[26],"UK":[28,153],"has":[31,47,85,193],"been":[32,106],"instructed":[33],"by":[34,42,119,241],"Drinking":[36],"Water":[37],"Inspectorate":[38],"to":[39,51,82,92,122,205,225,250],"be":[40,206,218,251],"lead-free":[41],"2050,":[43],"and":[44,110,129,141,210,232],"USEPA":[46],"assigned":[48],"$3":[49],"billion":[50],"tackle":[52,93],"lead.":[53,72],"The":[54,101,156,220],"first":[55],"key":[56],"challenge":[57,158],"in":[58,188,229],"achieving":[59],"this":[60,83,198,230],"determining":[63,189],"whether":[64],"property\u2019s":[66],"are":[69],"made":[70,87],"of":[71,78,89,97,164,236,245],"Previous":[73],"research":[74,228],"into":[75],"application":[77],"machine":[79,170],"learning":[80,171],"(ML)":[81],"problem":[84],"often":[86],"use":[88],"proxy":[90],"problems":[91],"core":[95],"identifying":[98],"SPs.":[100],"main":[102],"proxies":[103,118,166,180],"used":[104],"have":[105],"concentration":[108],"sampling":[109],"asset":[111],"records.":[112],"This":[113],"paper":[114],"will":[115,223],"evaluate":[116],"these":[117,165,179],"using":[120],"them":[121],"train":[123],"ML":[124],"models":[125,176],"as":[126],"target":[127],"classifications":[128],"comparing":[130],"results":[132,157],"with":[133],"those":[134],"from":[135,151,208],"model":[137,216],"trained":[138,177],"on":[139,145,178,197],"excavations":[140,209],"trial":[142,211],"holes":[143,212],"based":[144],"large":[147],"real-world":[148,199],"data":[149,200,203],"set":[150],"company.":[155],"assumptions":[160],"about":[161],"suitability":[163],"training":[168],"data-driven":[169],"models.":[172],"They":[173],"show":[174],"that":[175,248],"can":[181,217],"perform":[182],"no":[183],"better":[184],"than":[185],"random":[186],"chance":[187],"if":[190],"property":[192],"SP":[196,238],"set.":[201],"Instead,":[202],"need":[204,249],"collected":[207],"before":[213],"effective":[215],"trained.":[219],"conclusions":[221],"drawn":[222],"help":[224],"direct":[226],"future":[227],"area":[231],"improve":[233],"effectiveness":[235],"replacement":[239],"efforts":[240],"reducing":[242],"number":[244],"false":[246],"positives":[247],"visited.":[252]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-11T00:00:00"}