{"id":"https://openalex.org/W7160033088","doi":"https://doi.org/10.48550/arxiv.2605.00056","title":"Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution","display_name":"Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7160033088","doi":"https://doi.org/10.48550/arxiv.2605.00056"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00056","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00056","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.00056","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135197618","display_name":"T. Ansah-Narh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ansah-Narh, T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135233638","display_name":"G. Y. Afrifa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Afrifa, G. Y.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017778055","display_name":"J.B. Tandoh","orcid":"https://orcid.org/0000-0002-8398-1523"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tandoh, J. B.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135150965","display_name":"K. Asare","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Asare, K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129710936","display_name":"M. Addi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Addi, M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135112293","display_name":"K. E. Yorke","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yorke, K. E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135128582","display_name":"D. M. A. Akpoley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akpoley, D. M. A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053286600","display_name":"K. Aidoo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aidoo, K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135156585","display_name":"S. K. Fosuhene","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fosuhene, S. K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.7649000287055969,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.7649000287055969,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.060100000351667404,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10398","display_name":"Groundwater and Isotope Geochemistry","score":0.04769999906420708,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.6079000234603882},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6025000214576721},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5672000050544739},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5454999804496765},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.45489999651908875},{"id":"https://openalex.org/keywords/groundwater","display_name":"Groundwater","score":0.43209999799728394},{"id":"https://openalex.org/keywords/spatial-correlation","display_name":"Spatial correlation","score":0.4056999981403351},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.3824999928474426},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.36820000410079956}],"concepts":[{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.6079000234603882},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6025000214576721},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5672000050544739},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5454999804496765},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.46700000762939453},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.45489999651908875},{"id":"https://openalex.org/C76177295","wikidata":"https://www.wikidata.org/wiki/Q161598","display_name":"Groundwater","level":2,"score":0.43209999799728394},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4235000014305115},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.4056999981403351},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40549999475479126},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.31850001215934753},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31380000710487366},{"id":"https://openalex.org/C521259446","wikidata":"https://www.wikidata.org/wiki/Q58734","display_name":"Pollution","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.3100999891757965},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C17618745","wikidata":"https://www.wikidata.org/wiki/Q207509","display_name":"Copula (linguistics)","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26589998602867126},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.26440000534057617},{"id":"https://openalex.org/C120417685","wikidata":"https://www.wikidata.org/wiki/Q860333","display_name":"Flash flood","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00056","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00056","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.00056","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00056","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/6","score":0.6068968176841736,"display_name":"Clean water and sanitation"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Groundwater":[0],"in":[1],"the":[2,18,32,141],"Densu":[3],"Basin":[4],"is":[5,30,39],"increasingly":[6],"threatened":[7],"by":[8,44],"heavy":[9],"metal":[10],"contamination,":[11],"but":[12],"conventional":[13],"methods":[14],"fail":[15],"to":[16,48,77],"capture":[17],"statistical":[19],"complexity":[20],"and":[21,42,72,79,97,110,163,172,190,199],"spatial":[22,197],"heterogeneity":[23],"of":[24,213],"pollution":[25],"indicators.":[26],"A":[27],"key":[28],"challenge":[29],"modelling":[31],"Heavy":[33],"Metal":[34],"Pollution":[35],"Index":[36],"(HPI),":[37],"which":[38],"typically":[40],"skewed":[41],"affected":[43],"correlated":[45],"contaminants,":[46],"leading":[47],"biased":[49],"predictions":[50],"without":[51],"transformation.":[52],"This":[53],"study":[54],"develops":[55],"a":[56,98],"predictive":[57],"framework":[58],"integrating":[59],"response":[60],"transformations":[61,69],"with":[62,153,179,206],"nested":[63],"cross-validated":[64],"ensemble":[65,112,146],"machine":[66],"learning.":[67],"Three":[68],"(raw,":[70],"log,":[71],"Gaussian":[73,138],"copula)":[74],"were":[75],"applied":[76],"HPI":[78,176],"evaluated":[80],"across":[81],"six":[82],"learners:":[83],"support":[84],"vector":[85],"regression":[86],"(SVM),":[87],"$k$-nearest":[88],"neighbours":[89],"(k-NN),":[90],"CART,":[91],"Elastic":[92],"Net,":[93],"kernel":[94],"ridge":[95],"regression,":[96],"stacked":[99,111,145],"Lasso":[100],"ensemble.":[101],"Raw-scale":[102],"models":[103,160],"produced":[104,164],"deceptively":[105],"high":[106,157],"fits":[107],"(Elastic":[108],"Net":[109],"$R^2":[113,124,131,147],"\\approx":[114],"1.0$),":[115],"suggesting":[116],"over-optimism.":[117],"The":[118,137],"log":[119],"transformation":[120],"stabilised":[121],"variance":[122],"(SVM:":[123],"=":[125,132,148],"0.93$,":[126],"RMSE":[127,134],"$=":[128,135,151],"0.18$;":[129],"k-NN:":[130],"0.92$,":[133],"0.20$).":[136],"copula":[139],"gave":[140],"most":[142],"reliable":[143],"results:":[144],"0.96$":[149],"(RMSE":[150],"0.19$),":[152],"other":[154,200],"learners":[155],"maintaining":[156],"accuracy.":[158],"Copula-based":[159],"improved":[161],"residuals":[162],"spatially":[165],"plausible":[166],"maps.":[167],"DBSCAN":[168],"clustering":[169,207],"revealed":[170],"Fe":[171],"Mn":[173],"as":[174],"primary":[175],"contributors,":[177],"consistent":[178],"regional":[180],"hydrogeochemistry.":[181],"Limitations":[182],"include":[183],"reliance":[184],"on":[185],"random":[186],"(not":[187],"spatial)":[188],"cross-validation":[189],"basin-specific":[191],"scope.":[192],"Future":[193],"work":[194],"should":[195],"explore":[196],"validation":[198],"geological":[201],"settings.":[202],"Overall,":[203],"distribution-aware":[204],"ensembles":[205],"diagnostics":[208],"offer":[209],"robust,":[210],"interpretable":[211],"assessments":[212],"groundwater":[214],"contamination.":[215]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-05T00:00:00"}
