{"id":"https://openalex.org/W7128385005","doi":"https://doi.org/10.1016/j.ecoinf.2026.103650","title":"Explainable AI-based modeling of chlorophyll-a using water quality and DOM characteristics: Toward interpretable prediction and actionable management strategies","display_name":"Explainable AI-based modeling of chlorophyll-a using water quality and DOM characteristics: Toward interpretable prediction and actionable management strategies","publication_year":2026,"publication_date":"2026-02-08","ids":{"openalex":"https://openalex.org/W7128385005","doi":"https://doi.org/10.1016/j.ecoinf.2026.103650"},"language":"en","primary_location":{"id":"doi:10.1016/j.ecoinf.2026.103650","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2026.103650","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.ecoinf.2026.103650","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000694714","display_name":"Ji Woo Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ji Woo Han","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073058991","display_name":"Yeon Jung Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yeon Jung Cho","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047109103","display_name":"In Gu Ryu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"In Gu Ryu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125412129","display_name":"Ji-Hyun Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ji-Hyun Park","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102289710","display_name":"Taegu Kang","orcid":"https://orcid.org/0000-0001-7138-4046"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taegu Kang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5114056042","display_name":"JaYong Koo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jayong Koo","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5000694714"],"corresponding_institution_ids":[],"apc_list":{"value":2510,"currency":"USD","value_usd":2510},"apc_paid":{"value":2510,"currency":"USD","value_usd":2510},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28757721,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"94","issue":null,"first_page":"103650","last_page":"103650"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.38659998774528503,"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":0.38659998774528503,"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":0.06759999692440033,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing 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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.05009999871253967,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/water-quality","display_name":"Water quality","score":0.6025999784469604},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.450300008058548},{"id":"https://openalex.org/keywords/quality-management","display_name":"Quality management","score":0.3264999985694885},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.31310001015663147},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.29589998722076416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6189000010490417},{"id":"https://openalex.org/C2780797713","wikidata":"https://www.wikidata.org/wiki/Q625376","display_name":"Water quality","level":2,"score":0.6025999784469604},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.450300008058548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4307999908924103},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3986999988555908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33320000767707825},{"id":"https://openalex.org/C71405471","wikidata":"https://www.wikidata.org/wiki/Q757012","display_name":"Quality management","level":3,"score":0.3264999985694885},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2743000090122223},{"id":"https://openalex.org/C2992151728","wikidata":"https://www.wikidata.org/wiki/Q474883","display_name":"Water utility","level":3,"score":0.266400009393692}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1016/j.ecoinf.2026.103650","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2026.103650","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f1ec27ac6e6a4c979be7bc7eb07af3cc","is_oa":true,"landing_page_url":"https://doaj.org/article/f1ec27ac6e6a4c979be7bc7eb07af3cc","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Ecological Informatics, Vol 94, Iss , Pp 103650- (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.ecoinf.2026.103650","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2026.103650","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.799716591835022}],"awards":[{"id":"https://openalex.org/G3112798902","display_name":null,"funder_award_id":"NIER-2025-01-01-026","funder_id":"https://openalex.org/F4320322007","funder_display_name":"Ministry of Environment"}],"funders":[{"id":"https://openalex.org/F4320322007","display_name":"Ministry of Environment","ror":"https://ror.org/04xmt0833"},{"id":"https://openalex.org/F4320326584","display_name":"National Institute of Environmental Research","ror":"https://ror.org/02xhmzq41"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":105,"referenced_works":["https://openalex.org/W130099677","https://openalex.org/W1151842058","https://openalex.org/W1650271316","https://openalex.org/W1678356000","https://openalex.org/W1936209130","https://openalex.org/W1971230833","https://openalex.org/W1974198938","https://openalex.org/W1990812239","https://openalex.org/W2014900747","https://openalex.org/W2029673529","https://openalex.org/W2039240772","https://openalex.org/W2042267746","https://openalex.org/W2055571001","https://openalex.org/W2066579884","https://openalex.org/W2072846683","https://openalex.org/W2079927499","https://openalex.org/W2080209099","https://openalex.org/W2083183994","https://openalex.org/W2089715272","https://openalex.org/W2092376544","https://openalex.org/W2094293662","https://openalex.org/W2104166468","https://openalex.org/W2118020555","https://openalex.org/W2154237171","https://openalex.org/W2161310275","https://openalex.org/W2163025476","https://openalex.org/W2171509272","https://openalex.org/W2172148630","https://openalex.org/W2327993677","https://openalex.org/W2422330928","https://openalex.org/W2491382239","https://openalex.org/W2548514947","https://openalex.org/W2554612781","https://openalex.org/W2596911888","https://openalex.org/W2598238004","https://openalex.org/W2625769905","https://openalex.org/W2766520584","https://openalex.org/W2767335805","https://openalex.org/W2782593074","https://openalex.org/W2790245500","https://openalex.org/W2790459843","https://openalex.org/W2797930902","https://openalex.org/W2809190938","https://openalex.org/W2886975319","https://openalex.org/W2888753230","https://openalex.org/W2909206877","https://openalex.org/W2911964244","https://openalex.org/W2915998902","https://openalex.org/W2922100867","https://openalex.org/W2922218518","https://openalex.org/W2944737605","https://openalex.org/W2945099481","https://openalex.org/W2945432073","https://openalex.org/W2967517291","https://openalex.org/W2969873816","https://openalex.org/W2988781705","https://openalex.org/W2995976122","https://openalex.org/W3040806368","https://openalex.org/W3045466036","https://openalex.org/W3049655715","https://openalex.org/W3087054297","https://openalex.org/W3095239916","https://openalex.org/W3113374165","https://openalex.org/W3122583350","https://openalex.org/W3128364847","https://openalex.org/W3129493618","https://openalex.org/W3131703649","https://openalex.org/W3170629395","https://openalex.org/W3176342080","https://openalex.org/W3184607363","https://openalex.org/W3186318739","https://openalex.org/W4200350392","https://openalex.org/W4206154116","https://openalex.org/W4211010233","https://openalex.org/W4224989698","https://openalex.org/W4229008469","https://openalex.org/W4282593802","https://openalex.org/W4283705654","https://openalex.org/W4286432829","https://openalex.org/W4289522447","https://openalex.org/W4297200529","https://openalex.org/W4308972078","https://openalex.org/W4311391352","https://openalex.org/W4320477670","https://openalex.org/W4366262984","https://openalex.org/W4366588267","https://openalex.org/W4377141390","https://openalex.org/W4385776934","https://openalex.org/W4386024384","https://openalex.org/W4386028920","https://openalex.org/W4386142022","https://openalex.org/W4387299856","https://openalex.org/W4388333578","https://openalex.org/W4389233597","https://openalex.org/W4389262472","https://openalex.org/W4390639084","https://openalex.org/W4392469710","https://openalex.org/W4396510996","https://openalex.org/W4399388172","https://openalex.org/W4399522010","https://openalex.org/W4400674448","https://openalex.org/W4406070279","https://openalex.org/W4407837027","https://openalex.org/W4412507292","https://openalex.org/W4413209999"],"related_works":[],"abstract_inverted_index":{"Chlorophyll-a":[0],"(Chl-a)":[1],"is":[2],"a":[3,22],"key":[4,194],"indicator":[5],"of":[6,33,130,140,154,183,228],"eutrophication":[7],"and":[8,30,41,73,94,134,191,216,218,221],"algal":[9],"blooms":[10],"in":[11,59,157,230],"freshwater":[12,231],"ecosystems.":[13],"This":[14],"study":[15],"aimed":[16],"to":[17,132,172],"improve":[18],"Chl-a":[19,158,170,229],"prediction":[20,214],"through":[21],"novel":[23],"integrative":[24],"approach":[25,204],"that":[26],"combines":[27],"the":[28,60,106,141,152,174,181,197,225],"optical":[29,92],"structural":[31],"characteristics":[32,207],"dissolved":[34],"organic":[35,123],"matter":[36],"(DOM)":[37],"with":[38,208],"machine":[39],"learning":[40,66],"explainable":[42,209],"artificial":[43,210],"intelligence":[44,211],"techniques.":[45],"Weekly":[46],"data":[47],"were":[48,77],"collected":[49],"over":[50],"one":[51],"year":[52],"(Apr":[53],"2023\u2013Apr":[54],"2024)":[55],"from":[56,146],"four":[57],"sites":[58],"Yanghwa":[61],"Stream,":[62],"South":[63],"Korea.":[64],"Machine":[65],"models":[67],"(Random":[68],"Forest,":[69],"Gradient":[70,75,103],"Boosting":[71,104],"Machine,":[72],"Extreme":[74,102],"Boosting)":[76],"developed":[78],"using":[79],"34":[80],"input":[81],"variables":[82,184],"encompassing":[83],"15":[84],"traditional":[85],"water":[86],"quality":[87],"parameters,":[88],"8":[89],"ultraviolet\u2013visible":[90],"(UV\u2013Vis)":[91],"indices,":[93],"11":[95],"liquid":[96],"chromatography\u2013organic":[97],"carbon":[98],"detection":[99],"(LC\u2013OCD)":[100],"components.":[101],"showed":[105],"best":[107],"performance":[108],"(R2":[109],"=":[110,113],"0.7318,":[111],"RMSE":[112],"4.3827).":[114],"SHapley":[115],"Additive":[116],"exPlanations":[117],"analysis":[118,161],"identified":[119],"total":[120,122,187],"nitrogen,":[121,188],"carbon,":[124],"spectral":[125],"slope":[126],"ratio":[127,129],"(SR;":[128],"S275\u2013295":[131],"S350\u2013400),":[133],"Molecularity":[135],"(nominal":[136],"average":[137],"molecular":[138],"weight":[139],"humic":[142],"substances":[143],"fraction":[144],"derived":[145],"LC-OCD)":[147],"as":[148,186,193],"major":[149],"contributors,":[150],"highlighting":[151],"importance":[153,182],"DOM":[155,206],"composition":[156],"variability.":[159],"Counterfactual":[160],"further":[162],"revealed":[163],"model-guided":[164],"adjustment":[165],"scenarios":[166],"for":[167,224],"reducing":[168],"high":[169],"concentrations":[171],"below":[173],"threshold":[175],"(12.2":[176],"mg/m3;":[177],"75th":[178],"percentile),":[179],"emphasizing":[180],"such":[185],"phosphate,":[189],"Molecularity,":[190],"SR":[192],"indicators":[195],"within":[196],"learned":[198],"model.":[199],"In":[200],"conclusion,":[201],"this":[202],"integrated":[203],"combining":[205],"techniques":[212],"enhances":[213],"accuracy":[215],"interpretability":[217],"provides":[219],"specific":[220],"practical":[222],"strategies":[223],"effective":[226],"control":[227],"systems.":[232]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-02-10T00:00:00"}