{"id":"https://openalex.org/W4282935239","doi":"https://doi.org/10.3390/w14121899","title":"Urban River Dissolved Oxygen Prediction Model Using Machine Learning","display_name":"Urban River Dissolved Oxygen Prediction Model Using Machine Learning","publication_year":2022,"publication_date":"2022-06-13","ids":{"openalex":"https://openalex.org/W4282935239","doi":"https://doi.org/10.3390/w14121899"},"language":"en","primary_location":{"id":"doi:10.3390/w14121899","doi":"https://doi.org/10.3390/w14121899","is_oa":true,"landing_page_url":"https://doi.org/10.3390/w14121899","pdf_url":"https://www.mdpi.com/2073-4441/14/12/1899/pdf?version=1655371594","source":{"id":"https://openalex.org/S134216166","display_name":"Water","issn_l":"2073-4441","issn":["2073-4441"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Water","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-4441/14/12/1899/pdf?version=1655371594","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020488988","display_name":"Juhwan Moon","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Juhwan Moon","raw_affiliation_strings":["Interdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan University, Seoul 03063, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Interdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan University, Seoul 03063, Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101457198","display_name":"Jae\u2010Joon Lee","orcid":"https://orcid.org/0000-0003-2413-4057","observed_orcids":["https://orcid.org/0000-0003-2413-4057"]},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaejoon Lee","raw_affiliation_strings":["Interdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan University, Seoul 03063, Korea"],"raw_orcid":"https://orcid.org/0000-0003-2413-4057","affiliations":[{"raw_affiliation_string":"Interdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan University, Seoul 03063, Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360920","display_name":"Sangwon Lee","orcid":"https://orcid.org/0000-0003-3936-9342","observed_orcids":["https://orcid.org/0000-0003-3936-9342"]},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sangwon Lee","raw_affiliation_strings":["Interdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan University, Seoul 03063, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Interdisciplinary Program in Crisis, Disaster and Risk Management, Sungkyunkwan University, Seoul 03063, Korea","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113713719","display_name":"Hong\u2010Sik Yun","orcid":"https://orcid.org/0000-0002-2104-9423","observed_orcids":["https://orcid.org/0000-0002-2104-9423"]},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hongsik Yun","raw_affiliation_strings":["School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University, Seoul 03063, Korea"],"raw_orcid":"https://orcid.org/0000-0002-2104-9423","affiliations":[{"raw_affiliation_string":"School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University, Seoul 03063, Korea","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5113713719"],"corresponding_institution_ids":["https://openalex.org/I848706"],"apc_list":{"value":2200,"currency":"CHF","value_usd":2303},"apc_paid":{"value":2200,"currency":"CHF","value_usd":2303},"fwci":1.7984,"has_fulltext":true,"cited_by_count":29,"citation_normalized_percentile":{"value":0.82672962,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"14","issue":"12","first_page":"1899","last_page":"1899"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.8256999850273132,"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"}},"topics":[{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.8256999850273132,"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/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.09539999812841415,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Waste Management and Disposal"},"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/T11634","display_name":"Water Quality and Pollution Assessment","score":0.05429999902844429,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dissolved-oxygen-prediction","display_name":"dissolved oxygen prediction","score":0.921999990940094},{"id":"https://openalex.org/keywords/urban-streams","display_name":"urban rivers","score":0.8169999718666077},{"id":"https://openalex.org/keywords/water-quality","display_name":"water quality","score":0.6959999799728394},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.984000027179718},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9890000224113464},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.9959999918937683},{"id":"https://openalex.org/keywords/water-quality-index","display_name":"water quality index","score":0.6230000257492065},{"id":"https://openalex.org/keywords/water-pollution-assessment","display_name":"water pollution assessment","score":0.6539999842643738},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.6200000047683716},{"id":"https://openalex.org/keywords/ashrae-guideline-14","display_name":"ASHRAE Guideline 14","score":0.8190000057220459}],"concepts":[{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.7629355192184448},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6491037011146545},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6313601732254028},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5112111568450928},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4525735080242157},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.42639389634132385},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42459583282470703},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4127247631549835},{"id":"https://openalex.org/C206145494","wikidata":"https://www.wikidata.org/wiki/Q4654236","display_name":"ASHRAE 90.1","level":2,"score":0.41268134117126465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39999932050704956},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3893694281578064},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.38903549313545227},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.34827882051467896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3251715898513794},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.32509374618530273},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3236420452594757},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.17280426621437073},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.14448130130767822},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09114208817481995}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3390/w14121899","doi":"https://doi.org/10.3390/w14121899","is_oa":true,"landing_page_url":"https://doi.org/10.3390/w14121899","pdf_url":"https://www.mdpi.com/2073-4441/14/12/1899/pdf?version=1655371594","source":{"id":"https://openalex.org/S134216166","display_name":"Water","issn_l":"2073-4441","issn":["2073-4441"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Water","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3390/w14121899","doi":"https://doi.org/10.3390/w14121899","is_oa":true,"landing_page_url":"https://doi.org/10.3390/w14121899","pdf_url":"https://www.mdpi.com/2073-4441/14/12/1899/pdf?version=1655371594","source":{"id":"https://openalex.org/S134216166","display_name":"Water","issn_l":"2073-4441","issn":["2073-4441"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Water","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":1.0}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320327527","display_name":"Ministry of the Interior and Safety","ror":"https://ror.org/010gtx568"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4282935239.pdf","grobid_xml":"https://content.openalex.org/works/W4282935239.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1534477342","https://openalex.org/W1678356000","https://openalex.org/W1972949757","https://openalex.org/W1988790447","https://openalex.org/W1992819723","https://openalex.org/W2011106911","https://openalex.org/W2017978747","https://openalex.org/W2056610423","https://openalex.org/W2077592630","https://openalex.org/W2089756368","https://openalex.org/W2101234009","https://openalex.org/W2133160781","https://openalex.org/W2172148630","https://openalex.org/W2261059368","https://openalex.org/W2295598076","https://openalex.org/W2327895505","https://openalex.org/W2344277455","https://openalex.org/W2402451342","https://openalex.org/W2524347453","https://openalex.org/W2768348081","https://openalex.org/W2795411881","https://openalex.org/W2815792770","https://openalex.org/W2885442465","https://openalex.org/W2886951474","https://openalex.org/W2894615779","https://openalex.org/W2915661318","https://openalex.org/W2916771010","https://openalex.org/W2930669685","https://openalex.org/W2955631626","https://openalex.org/W2988764837","https://openalex.org/W3048520747","https://openalex.org/W3085248027","https://openalex.org/W3087305874","https://openalex.org/W3109154439","https://openalex.org/W3129160110","https://openalex.org/W3191896836","https://openalex.org/W4220951571","https://openalex.org/W4241005218","https://openalex.org/W4388423198"],"related_works":["https://openalex.org/W4408164410","https://openalex.org/W3177661601","https://openalex.org/W2617137052","https://openalex.org/W3149793055","https://openalex.org/W4396769179","https://openalex.org/W4379094562","https://openalex.org/W3176787227","https://openalex.org/W4400990059","https://openalex.org/W2020656512","https://openalex.org/W4414434498"],"abstract_inverted_index":{"This":[0],"study":[1],"outlines":[2],"the":[3,7,14,18,22,27,33,40,70,96,100,109,123,128,133,141,145,149,169,172,188,193],"preliminary":[4],"stages":[5],"of":[6,9,17,35,99,148,168,171,195,222],"development":[8],"an":[10,113],"algorithm":[11,29,48,114,139],"to":[12,31,127,209,212],"predict":[13,32,69],"optimal":[15],"WQ":[16,214],"Hwanggujicheon":[19],"Stream.":[20],"In":[21],"first":[23],"stages,":[24],"we":[25,73],"used":[26,107],"AdaBoost":[28,47,101],"model":[30,54,153,190],"state":[34],"WQ,":[36,72],"using":[37,132],"data":[38],"from":[39],"open":[41],"artificial":[42],"intelligence":[43],"(AI)":[44],"hub.":[45],"The":[46,166],"has":[49],"excellent":[50,116],"predictive":[51,117,152,189],"performance":[52,146],"and":[53,56,62,93,162,182,204,215],"suitability":[55],"was":[57,106,119,157,160,164,178],"selected":[58,74,120],"for":[59],"random":[60,134],"forest":[61,135],"gradient":[63],"boosting":[64,66],"(GB)-based":[65],"models.":[67],"To":[68],"optimized":[71],"pH,":[75],"SS,":[76],"water":[77],"temperature,":[78],"total":[79,82,90],"nitrogen(TN),":[80],"dissolved":[81,89],"phosphorus(DTP),":[83],"NH3-N,":[84],"chemical":[85],"oxygen":[86,104],"demand":[87],"(COD),":[88],"nitrogen":[91],"(DTN),":[92],"NO3-N":[94],"as":[95,108],"input":[97,129],"variables":[98],"model.":[102,143],"Dissolved":[103],"(DO)":[105],"target":[110],"variable.":[111],"Third,":[112],"showing":[115],"power":[118],"by":[121,131],"analyzing":[122],"prediction":[124],"accuracy":[125],"according":[126],"variable":[130],"or":[136],"GB":[137],"series":[138],"in":[140,218],"initial":[142],"Finally,":[144],"evaluation":[147],"ultimately":[150],"developed":[151,191],"demonstrated":[154],"that":[155,187,202],"RMS":[156],"0.015,":[158],"MAE":[159],"0.009,":[161],"R2":[163,180],"0.912.":[165],"coefficient":[167],"variation":[170],"root":[173],"mean":[174],"square":[175],"error":[176],"(CVRMSE)":[177],"17.404.":[179],"0.912":[181],"CVRMSE":[183],"were":[184],"17.404,":[185],"indicating":[186],"meets":[192],"criteria":[194],"ASHRAE":[196],"Guideline":[197],"14.":[198],"It":[199],"is":[200],"imperative":[201],"government":[203],"administrative":[205],"agencies":[206],"have":[207],"access":[208],"effective":[210],"tools":[211],"assess":[213],"pollution":[216],"levels":[217],"their":[219],"local":[220],"bodies":[221],"water.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":4}],"updated_date":"2026-11-13T13:42:25.865203","created_date":"2022-06-16T00:00:00"}
