{"id":"https://openalex.org/W4297796144","doi":"https://doi.org/10.1145/3548785.3548809","title":"A comparative study of three deep learning models for PM2.5 interpolation","display_name":"A comparative study of three deep learning models for PM2.5 interpolation","publication_year":2022,"publication_date":"2022-08-22","ids":{"openalex":"https://openalex.org/W4297796144","doi":"https://doi.org/10.1145/3548785.3548809"},"language":"en","primary_location":{"id":"doi:10.1145/3548785.3548809","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3548785.3548809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th International Database Engineered Applications Symposium","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/A5100782093","display_name":"Lixin Li","orcid":"https://orcid.org/0000-0002-9980-2649"},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lixin Li","raw_affiliation_strings":["Department of Computer Science, Georgia Southern University, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Georgia Southern University, USA","institution_ids":["https://openalex.org/I39815113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100782093"],"corresponding_institution_ids":["https://openalex.org/I39815113"],"apc_list":null,"apc_paid":null,"fwci":0.0852,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.37230585,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"16","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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/T10190","display_name":"Air Quality and Health Impacts","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"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/T12095","display_name":"Vehicle emissions and performance","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.7781058549880981},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6127419471740723},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5903357267379761},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5493780970573425},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.49114057421684265},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48762646317481995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43964913487434387},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3757389187812805},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34375855326652527},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3222562074661255}],"concepts":[{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.7781058549880981},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6127419471740723},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5903357267379761},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5493780970573425},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.49114057421684265},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48762646317481995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43964913487434387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3757389187812805},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34375855326652527},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3222562074661255},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3548785.3548809","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3548785.3548809","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th International Database Engineered Applications Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1565649072","https://openalex.org/W1888223309","https://openalex.org/W2014793248","https://openalex.org/W2041580383","https://openalex.org/W2089625383","https://openalex.org/W2131774270","https://openalex.org/W2321069189","https://openalex.org/W2510642588","https://openalex.org/W2550143307","https://openalex.org/W2729281059","https://openalex.org/W2783920628","https://openalex.org/W2789876780","https://openalex.org/W2795798228","https://openalex.org/W2812669263","https://openalex.org/W2898461917","https://openalex.org/W2909340263","https://openalex.org/W2963103134","https://openalex.org/W2978016765","https://openalex.org/W3009685637","https://openalex.org/W3011590044","https://openalex.org/W4213176465","https://openalex.org/W4213255817","https://openalex.org/W4237999661","https://openalex.org/W4300564909"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2397288865","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"PM2.5":[0,82,108,149],"is":[1,45,145],"a":[2,46,139],"pollutant":[3,57],"particulate":[4],"matter":[5],"with":[6],"diameter":[7],"less":[8],"than":[9],"2.5":[10],"micrometer.":[11],"There":[12],"exist":[13],"many":[14],"stations":[15],"installed":[16],"in":[17,58],"the":[18,53,56,107,111,115,124,130,142],"world":[19],"to":[20,39,51,80,105],"measure":[21],"its":[22,41],"concentration.":[23,42],"Some":[24],"areas":[25,73],"without":[26],"any":[27,31],"proper":[28],"equipment":[29],"nor":[30],"station":[32],"installation":[33],"must":[34],"rely":[35],"on":[36,129,141],"interpolation":[37,49,66],"techniques":[38,122],"approximate":[40,52],"So,":[43],"there":[44],"need":[47],"of":[48,55,114,126],"technique":[50,67],"concentration":[54,109],"those":[59],"areas.":[60],"The":[61],"faster":[62],"and":[63,74,123,137],"more":[64,71,146],"accurate":[65],"can":[68],"help":[69],"identify":[70],"polluted":[72],"thus":[75],"efficiently":[76],"take":[77],"some":[78],"measures":[79],"reduce":[81],"harmful":[83],"effects.":[84],"We":[85,117,132],"explored":[86],"three":[87],"different":[88,119],"neural":[89],"networks,":[90],"i.e.,":[91],"Bidirectional-Long":[92],"Short-Term":[93],"Memory":[94],"(Bi-LSTM),":[95],"Gated":[96],"Recurrent":[97],"Unit":[98],"(GRU),":[99],"Temporal":[100],"Convolutional":[101],"Neural":[102],"Networks":[103],"(TCN),":[104],"interpolate":[106],"over":[110],"southeast":[112],"region":[113],"U.S.":[116],"investigate":[118],"data":[120],"preprocessing":[121],"effects":[125],"spatiotemporal":[127],"correlation":[128],"models.":[131],"finally":[133],"compare":[134],"these":[135],"models":[136],"make":[138],"choice":[140],"model":[143],"that":[144],"appropriate":[147],"for":[148],"interpolation.":[150]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
