{"id":"https://openalex.org/W7161994406","doi":"https://doi.org/10.1007/s10489-026-07278-y","title":"An explainable deep learning method based on spectral co-clustering for ozone time series forecasting","display_name":"An explainable deep learning method based on spectral co-clustering for ozone time series forecasting","publication_year":2026,"publication_date":"2026-05-21","ids":{"openalex":"https://openalex.org/W7161994406","doi":"https://doi.org/10.1007/s10489-026-07278-y"},"language":"en","primary_location":{"id":"doi:10.1007/s10489-026-07278-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-026-07278-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-026-07278-y.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10489-026-07278-y.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136722889","display_name":"Naeem Ullah","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117450","display_name":"Federico II University Hospital","ror":"https://ror.org/02jr6tp70","country_code":"IT","type":"healthcare","lineage":["https://openalex.org/I4210117450","https://openalex.org/I71267560"]},{"id":"https://openalex.org/I71267560","display_name":"University of Naples Federico II","ror":"https://ror.org/05290cv24","country_code":"IT","type":"education","lineage":["https://openalex.org/I71267560"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Naeem Ullah","raw_affiliation_strings":["Department of Electrical Engineering and Information Technology, University of Naples Federico II, via Claudio 21, Naples, 80125, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Information Technology, University of Naples Federico II, via Claudio 21, Naples, 80125, Italy","institution_ids":["https://openalex.org/I71267560","https://openalex.org/I4210117450"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122912881","display_name":"Francisco Mart\u00ednez-\u00c1lvarez","orcid":null},"institutions":[{"id":"https://openalex.org/I95013407","display_name":"Universidad Pablo de Olavide","ror":"https://ror.org/02z749649","country_code":"ES","type":"education","lineage":["https://openalex.org/I95013407"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Francisco Mart\u00ednez-\u00c1lvarez","raw_affiliation_strings":["Data Science and Big Data Lab, Pablo de Olavide University, Ctra. de Utrera 1, Seville, 41013, Spain"],"raw_orcid":"https://orcid.org/0000-0002-6309-1785","affiliations":[{"raw_affiliation_string":"Data Science and Big Data Lab, Pablo de Olavide University, Ctra. de Utrera 1, Seville, 41013, Spain","institution_ids":["https://openalex.org/I95013407"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008040085","display_name":"Ivanoe De Falco","orcid":"https://orcid.org/0000-0001-6127-1195"},"institutions":[{"id":"https://openalex.org/I3005160176","display_name":"Institute for High Performance Computing and Networking","ror":"https://ror.org/04r5fge26","country_code":"IT","type":"facility","lineage":["https://openalex.org/I3005160176","https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Ivanoe De\u00a0Falco","raw_affiliation_strings":["Institute for High-Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), via Pietro Castellino 111, Naples, 80131, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for High-Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), via Pietro Castellino 111, Naples, 80131, Italy","institution_ids":["https://openalex.org/I3005160176"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5136659525","display_name":"Giovanna Sannino","orcid":null},"institutions":[{"id":"https://openalex.org/I3005160176","display_name":"Institute for High Performance Computing and Networking","ror":"https://ror.org/04r5fge26","country_code":"IT","type":"facility","lineage":["https://openalex.org/I3005160176","https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Giovanna Sannino","raw_affiliation_strings":["Institute for High-Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), via Pietro Castellino 111, Naples, 80131, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for High-Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), via Pietro Castellino 111, Naples, 80131, Italy","institution_ids":["https://openalex.org/I3005160176"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5122912881"],"corresponding_institution_ids":["https://openalex.org/I95013407"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.90506946,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":"56","issue":"8","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.8659999966621399,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.8659999966621399,"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/T11320","display_name":"Atmospheric Ozone and Climate","score":0.02449999935925007,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10075","display_name":"Atmospheric chemistry and aerosols","score":0.019300000742077827,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"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/interpretability","display_name":"Interpretability","score":0.8657000064849854},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7809000015258789},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5198000073432922},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48350000381469727},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.46389999985694885},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46160000562667847},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.4318000078201294},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3723999857902527}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8657000064849854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8288000226020813},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7809000015258789},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5995000004768372},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5347999930381775},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5198000073432922},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48350000381469727},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.46389999985694885},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46160000562667847},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C2776349674","wikidata":"https://www.wikidata.org/wiki/Q2298060","display_name":"Tropospheric ozone","level":3,"score":0.36469998955726624},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29739999771118164},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2831000089645386},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10489-026-07278-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-026-07278-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-026-07278-y.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10489-026-07278-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10489-026-07278-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10489-026-07278-y.pdf","source":{"id":"https://openalex.org/S74726891","display_name":"Applied Intelligence","issn_l":"0924-669X","issn":["0924-669X","1573-7497"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1783447829","display_name":null,"funder_award_id":"PID2023-146037OB-C2","funder_id":"https://openalex.org/F4320322930","funder_display_name":"Ministerio de Ciencia e Innovaci\u00f3n"}],"funders":[{"id":"https://openalex.org/F4320322930","display_name":"Ministerio de Ciencia e Innovaci\u00f3n","ror":"https://ror.org/034900433"},{"id":"https://openalex.org/F4320326754","display_name":"Junta de Andaluc\u00eda","ror":"https://ror.org/01jem9c82"},{"id":"https://openalex.org/F4970757149","display_name":"Universidad Pablo de Olavide","ror":"https://ror.org/02z749649"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7161994406.pdf","grobid_xml":"https://content.openalex.org/works/W7161994406.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W119919366","https://openalex.org/W1787224781","https://openalex.org/W1986241067","https://openalex.org/W1990887604","https://openalex.org/W2064186732","https://openalex.org/W2110309989","https://openalex.org/W2171322452","https://openalex.org/W2587674390","https://openalex.org/W2752961258","https://openalex.org/W2766585573","https://openalex.org/W2888431710","https://openalex.org/W2963608065","https://openalex.org/W2979040987","https://openalex.org/W3015177549","https://openalex.org/W3120669297","https://openalex.org/W3127141142","https://openalex.org/W3146613606","https://openalex.org/W3186962463","https://openalex.org/W4200515036","https://openalex.org/W4210603697","https://openalex.org/W4214849673","https://openalex.org/W4296353071","https://openalex.org/W4309632781","https://openalex.org/W4313827979","https://openalex.org/W4322619709","https://openalex.org/W4323073185","https://openalex.org/W4381250019","https://openalex.org/W4391176491","https://openalex.org/W4392202934","https://openalex.org/W4393098663","https://openalex.org/W4399542752","https://openalex.org/W4405868276","https://openalex.org/W4406229784","https://openalex.org/W4406263258","https://openalex.org/W4406678595","https://openalex.org/W4407361804","https://openalex.org/W4409269128","https://openalex.org/W7124320029"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Tropospheric":[1],"ozone":[2,239,248],"forecasting":[3,77,202,249],"is":[4,116,124,250],"critical":[5,139],"for":[6,68,84,247],"public":[7],"health,":[8],"yet":[9],"the":[10,29,195,211,217,232],"deep":[11,103],"learning":[12,104],"models":[13],"that":[14,71,129,145,194],"achieve":[15],"high":[16,201],"accuracy":[17,203],"often":[18],"function":[19],"as":[20],"black":[21],"boxes.":[22],"This":[23],"lack":[24],"of":[25,31,187,210,231],"transparency,":[26],"along":[27],"with":[28,65,184,238,252],"inability":[30],"popular":[32],"explainability":[33],"techniques":[34],"like":[35],"SHapley":[36],"Additive":[37],"exPlanations":[38],"(SHAP)":[39],"to":[40,75,118,173,190,257],"capture":[41],"essential":[42],"temporal":[43,143,181,233],"dependencies,":[44],"limits":[45],"their":[46],"practical":[47],"utility":[48],"and":[49,79,99,102,111,133,142,220,234,244],"trustworthiness":[50],"in":[51],"environmental":[52,259],"management.":[53],"To":[54],"address":[55],"this,":[56],"we":[57],"propose":[58],"a":[59,125,180,242,253],"novel":[60],"framework,":[61],"eXplainable":[62],"Deep":[63],"Learning":[64],"Spectral":[66],"Co-clustering":[67],"Time":[69],"Series,":[70],"integrates":[72],"spectral":[73,126],"co-clustering":[74,127],"enhance":[76],"performance":[78,185],"provide":[80],"post-hoc":[81,207],"structured":[82],"interpretability":[83],"air-quality":[85],"time":[86,134,221,260],"series.":[87],"The":[88,121,148,164],"methodology":[89],"comprises":[90],"data":[91,157],"preprocessing,":[92],"feature":[93],"engineering":[94],"(including":[95],"lagging,":[96],"rolling":[97],"statistics,":[98],"time-based":[100],"features),":[101],"architectures":[105],"(Multilayer":[106],"Perceptron,":[107],"Gated":[108],"Recurrent":[109],"Unit,":[110],"hybrid":[112],"models).":[113],"Bayesian":[114],"optimization":[115],"used":[117],"fine-tune":[119],"hyperparameters.":[120],"core":[122],"contribution":[123],"technique":[128],"simultaneously":[130],"partitions":[131],"features":[132,219],"instances":[135],"into":[136],"co-clusters,":[137],"revealing":[138],"inter-feature":[140],"relationships":[141],"patterns":[144],"drive":[146],"predictions.":[147],"framework":[149],"was":[150],"rigorously":[151],"validated":[152],"through":[153,206],"extensive":[154],"experiments":[155],"on":[156],"from":[158,171],"five":[159],"air":[160],"quality":[161],"monitoring":[162],"stations.":[163],"proposed":[165,196],"approach":[166,197],"achieved":[167],"RMSE":[168],"values":[169],"ranging":[170],"0.73":[172],"6.08,":[174],"significantly":[175],"outperforming":[176],"existing":[177],"methods,":[178],"including":[179],"LSTM":[182],"baseline,":[183],"improvements":[186],"approximately":[188],"59.19%":[189],"95.65%.":[191],"Results":[192],"demonstrate":[193],"not":[198],"only":[199],"achieves":[200],"but":[204],"also,":[205],"heatmap":[208],"visualizations":[209],"objectively":[212],"selected":[213],"best-performing":[214],"co-cluster,":[215],"identifies":[216],"key":[218],"periods":[222],"governing":[223],"model":[224],"predictions,":[225],"thereby":[226],"offering":[227],"an":[228],"interpretable":[229],"understanding":[230],"feature-level":[235],"drivers":[236],"associated":[237],"variability.":[240],"Thus,":[241],"transparent":[243],"effective":[245],"solution":[246],"proposed,":[251],"modular":[254],"design":[255],"generalizable":[256],"other":[258],"series":[261],"prediction":[262],"tasks.":[263]},"counts_by_year":[],"updated_date":"2026-05-23T06:10:36.450269","created_date":"2026-05-22T00:00:00"}