{"id":"https://openalex.org/W4312290744","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892446","title":"Imputation of Partially Observed Water Quality Data Using Self-Attention LSTM","display_name":"Imputation of Partially Observed Water Quality Data Using Self-Attention LSTM","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312290744","doi":"https://doi.org/10.1109/ijcnn55064.2022.9892446"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9892446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892446","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5032582239","display_name":"Onatkut Dagtekin","orcid":null},"institutions":[{"id":"https://openalex.org/I191240316","display_name":"University of Hull","ror":"https://ror.org/04nkhwh30","country_code":"GB","type":"education","lineage":["https://openalex.org/I191240316"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Onatkut Dagtekin","raw_affiliation_strings":["University of Hull,Department of Computer Science and Technology,Hull,UK","Department of Computer Science and Technology, University of Hull, Hull, UK"],"affiliations":[{"raw_affiliation_string":"University of Hull,Department of Computer Science and Technology,Hull,UK","institution_ids":["https://openalex.org/I191240316"]},{"raw_affiliation_string":"Department of Computer Science and Technology, University of Hull, Hull, UK","institution_ids":["https://openalex.org/I191240316"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015800938","display_name":"Nina Dethlefs","orcid":"https://orcid.org/0000-0002-6917-5066"},"institutions":[{"id":"https://openalex.org/I191240316","display_name":"University of Hull","ror":"https://ror.org/04nkhwh30","country_code":"GB","type":"education","lineage":["https://openalex.org/I191240316"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nina Dethlefs","raw_affiliation_strings":["University of Hull,Department of Computer Science and Technology,Hull,UK","Department of Computer Science and Technology, University of Hull, Hull, UK"],"affiliations":[{"raw_affiliation_string":"University of Hull,Department of Computer Science and Technology,Hull,UK","institution_ids":["https://openalex.org/I191240316"]},{"raw_affiliation_string":"Department of Computer Science and Technology, University of Hull, Hull, UK","institution_ids":["https://openalex.org/I191240316"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5032582239"],"corresponding_institution_ids":["https://openalex.org/I191240316"],"apc_list":null,"apc_paid":null,"fwci":1.9366,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.87466234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.996399998664856,"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":0.996399998664856,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9940999746322632,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9926000237464905,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8324031233787537},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7641605734825134},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7194297909736633},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.574078381061554},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.49255308508872986},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48898112773895264},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48428791761398315},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44031086564064026},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.430547833442688},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.41860058903694153},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.19263219833374023},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11976629495620728}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8324031233787537},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7641605734825134},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7194297909736633},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.574078381061554},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.49255308508872986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48898112773895264},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48428791761398315},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44031086564064026},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.430547833442688},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41860058903694153},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19263219833374023},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11976629495620728}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9892446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9892446","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.8600000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1998780131","https://openalex.org/W2043565527","https://openalex.org/W2058720760","https://openalex.org/W2072356364","https://openalex.org/W2084415244","https://openalex.org/W2093713614","https://openalex.org/W2096863518","https://openalex.org/W2103972604","https://openalex.org/W2115022005","https://openalex.org/W2186440592","https://openalex.org/W2235053778","https://openalex.org/W2247991820","https://openalex.org/W2469618837","https://openalex.org/W2510914069","https://openalex.org/W2550179689","https://openalex.org/W2741295496","https://openalex.org/W2784220415","https://openalex.org/W2803403013","https://openalex.org/W2803805253","https://openalex.org/W2890686416","https://openalex.org/W2897020959","https://openalex.org/W2897574832","https://openalex.org/W2926585089","https://openalex.org/W2939939530","https://openalex.org/W2939948029","https://openalex.org/W2943198600","https://openalex.org/W2951565755","https://openalex.org/W2955443275","https://openalex.org/W2962932373","https://openalex.org/W2968249981","https://openalex.org/W2971179953","https://openalex.org/W2977676495","https://openalex.org/W2993187153","https://openalex.org/W3003365835","https://openalex.org/W3013950266","https://openalex.org/W3157129049","https://openalex.org/W3193645618","https://openalex.org/W3198445485","https://openalex.org/W4239015571","https://openalex.org/W4385245566","https://openalex.org/W6677019852","https://openalex.org/W6682042988","https://openalex.org/W6739901393","https://openalex.org/W6751145664","https://openalex.org/W6752046673","https://openalex.org/W6754349710","https://openalex.org/W6758554162","https://openalex.org/W6775095138"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W3123177881"],"abstract_inverted_index":{"Possible":[0],"sensory":[1],"failures":[2],"in":[3,7,50,97,102,120],"monitoring":[4],"systems":[5,22],"result":[6],"partially":[8],"filled":[9],"data":[10,43,70],"which":[11,71,98],"may":[12,19],"lead":[13],"to":[14,35,144],"erroneous":[15],"statistical":[16],"conclusions.":[17],"This":[18,38],"affect":[20],"critical":[21],"such":[23],"as":[24],"pollutant":[25],"detectors":[26],"and":[27,83],"anomaly":[28],"activity":[29],"detectors.":[30],"Therefore,":[31],"imputation":[32,99,134],"becomes":[33],"necessary":[34],"decrease":[36],"error.":[37],"work":[39],"addresses":[40],"the":[41,51,69,123,130,133,140],"missing":[42],"problem":[44],"by":[45,78],"experimenting":[46],"with":[47,58,93,110],"various":[48],"methods":[49],"context":[52],"of":[53,122,132,139],"a":[54,103,111],"water":[55],"quality":[56],"dataset":[57],"high":[59],"miss":[60],"rates.":[61],"Compared":[62],"models":[63],"chosen":[64],"make":[65],"different":[66],"assumptions":[67],"about":[68],"are":[72],"Generative":[73],"Adversarial":[74],"Networks,":[75],"Multiple":[76],"Imputation":[77],"Chained":[79],"Equations,":[80],"Variational":[81],"Auto-Encoders,":[82],"Recurrent":[84],"Neural":[85],"Networks.":[86],"A":[87],"novel":[88],"recurrent":[89],"neural":[90],"network":[91],"architecture":[92],"self-attention":[94,127],"is":[95,100],"proposed":[96,107],"done":[101],"single":[104],"pass.":[105],"The":[106,126],"model":[108],"performs":[109],"lower":[112],"root":[113],"mean":[114],"square":[115],"error,":[116],"ranging":[117],"between":[118],"0.012-0.28,":[119],"three":[121],"four":[124],"locations.":[125],"components":[128],"increase":[129],"interpretability":[131],"process":[135],"at":[136],"each":[137],"stage":[138],"network,":[141],"providing":[142],"information":[143],"domain":[145],"experts.":[146]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}