{"id":"https://openalex.org/W3091525140","doi":"https://doi.org/10.1155/2020/8834699","title":"Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing","display_name":"Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: A Case Study of Beijing","publication_year":2020,"publication_date":"2020-09-30","ids":{"openalex":"https://openalex.org/W3091525140","doi":"https://doi.org/10.1155/2020/8834699","mag":"3091525140","pmid":"https://pubmed.ncbi.nlm.nih.gov/33061948"},"language":"en","primary_location":{"id":"doi:10.1155/2020/8834699","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/8834699","pdf_url":"https://downloads.hindawi.com/journals/cin/2020/8834699.pdf","source":{"id":"https://openalex.org/S72372694","display_name":"Computational Intelligence and Neuroscience","issn_l":"1687-5265","issn":["1687-5265","1687-5273"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence and Neuroscience","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://downloads.hindawi.com/journals/cin/2020/8834699.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061377547","display_name":"Bingchun Liu","orcid":"https://orcid.org/0000-0002-0309-1302"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bingchun Liu","raw_affiliation_strings":["School of Management, Tianjin University of Technology, Tianjin 300384, China"],"raw_orcid":"https://orcid.org/0000-0002-0309-1302","affiliations":[{"raw_affiliation_string":"School of Management, Tianjin University of Technology, Tianjin 300384, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103259194","display_name":"Xiaoling Guo","orcid":"https://orcid.org/0000-0001-8558-7792"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoling Guo","raw_affiliation_strings":["School of Management, Tianjin University of Technology, Tianjin 300384, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management, Tianjin University of Technology, Tianjin 300384, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011314073","display_name":"Mingzhao Lai","orcid":"https://orcid.org/0009-0004-5426-5730"},"institutions":[{"id":"https://openalex.org/I136765683","display_name":"Tianjin University of Technology","ror":"https://ror.org/00zbe0w13","country_code":"CN","type":"education","lineage":["https://openalex.org/I136765683"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingzhao Lai","raw_affiliation_strings":["School of Management, Tianjin University of Technology, Tianjin 300384, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Management, Tianjin University of Technology, Tianjin 300384, China","institution_ids":["https://openalex.org/I136765683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100767290","display_name":"Qingshan Wang","orcid":"https://orcid.org/0000-0002-7559-067X"},"institutions":[{"id":"https://openalex.org/I11849452","display_name":"Tianjin Agricultural University","ror":"https://ror.org/0010b6s72","country_code":"CN","type":"education","lineage":["https://openalex.org/I11849452"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingshan Wang","raw_affiliation_strings":["School of Humanities, Tianjin Agricultural University, Tianjin 300384, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Humanities, Tianjin Agricultural University, Tianjin 300384, China","institution_ids":["https://openalex.org/I11849452"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061377547"],"corresponding_institution_ids":["https://openalex.org/I136765683"],"apc_list":{"value":2100,"currency":"USD","value_usd":2100},"apc_paid":{"value":2100,"currency":"USD","value_usd":2100},"fwci":0.6823,"has_fulltext":true,"cited_by_count":15,"citation_normalized_percentile":{"value":0.67241434,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"2020","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":1.0,"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":1.0,"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":0.9988999962806702,"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":0.9879999756813049,"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/beijing","display_name":"Beijing","score":0.6930586099624634},{"id":"https://openalex.org/keywords/pollutant","display_name":"Pollutant","score":0.6049631237983704},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5291381478309631},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5045286417007446},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4944131374359131},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46552059054374695},{"id":"https://openalex.org/keywords/air-pollution","display_name":"Air pollution","score":0.46090826392173767},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4499715566635132},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.41974249482154846},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.3946765959262848},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.39416998624801636},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33659827709198},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3262200951576233},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25040459632873535},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.07241514325141907}],"concepts":[{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.6930586099624634},{"id":"https://openalex.org/C82685317","wikidata":"https://www.wikidata.org/wiki/Q19829510","display_name":"Pollutant","level":2,"score":0.6049631237983704},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5291381478309631},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5045286417007446},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4944131374359131},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46552059054374695},{"id":"https://openalex.org/C559116025","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollution","level":2,"score":0.46090826392173767},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4499715566635132},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.41974249482154846},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.3946765959262848},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.39416998624801636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33659827709198},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3262200951576233},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25040459632873535},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.07241514325141907},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000068476","descriptor_name":"Beijing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000068476","descriptor_name":"Beijing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000068476","descriptor_name":"Beijing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000068476","descriptor_name":"Beijing","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000393","descriptor_name":"Air Pollutants","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":true},{"descriptor_ui":"D000393","descriptor_name":"Air Pollutants","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":true},{"descriptor_ui":"D000393","descriptor_name":"Air Pollutants","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":true},{"descriptor_ui":"D000393","descriptor_name":"Air Pollutants","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":true},{"descriptor_ui":"D000397","descriptor_name":"Air Pollution","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":true},{"descriptor_ui":"D000397","descriptor_name":"Air Pollution","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":true},{"descriptor_ui":"D000397","descriptor_name":"Air Pollution","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":true},{"descriptor_ui":"D000397","descriptor_name":"Air Pollution","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":true},{"descriptor_ui":"D004784","descriptor_name":"Environmental Monitoring","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004784","descriptor_name":"Environmental Monitoring","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004784","descriptor_name":"Environmental Monitoring","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004784","descriptor_name":"Environmental Monitoring","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008570","descriptor_name":"Memory, Short-Term","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008570","descriptor_name":"Memory, Short-Term","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D052638","descriptor_name":"Particulate Matter","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":false},{"descriptor_ui":"D052638","descriptor_name":"Particulate Matter","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":false},{"descriptor_ui":"D052638","descriptor_name":"Particulate Matter","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":false},{"descriptor_ui":"D052638","descriptor_name":"Particulate Matter","qualifier_ui":"Q000032","qualifier_name":"analysis","is_major_topic":false},{"descriptor_ui":"D058067","descriptor_name":"Wavelet Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058067","descriptor_name":"Wavelet Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058067","descriptor_name":"Wavelet Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058067","descriptor_name":"Wavelet Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1155/2020/8834699","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/8834699","pdf_url":"https://downloads.hindawi.com/journals/cin/2020/8834699.pdf","source":{"id":"https://openalex.org/S72372694","display_name":"Computational Intelligence and Neuroscience","issn_l":"1687-5265","issn":["1687-5265","1687-5273"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence and Neuroscience","raw_type":"journal-article"},{"id":"pmid:33061948","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33061948","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational intelligence and neuroscience","raw_type":null},{"id":"pmh:oai:doaj.org/article:a453a671ce6a493499a8535cd919c2cf","is_oa":false,"landing_page_url":"https://doaj.org/article/a453a671ce6a493499a8535cd919c2cf","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computational Intelligence and Neuroscience, Vol 2020 (2020)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:7545461","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7545461","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Comput Intell Neurosci","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1155/2020/8834699","is_oa":true,"landing_page_url":"https://doi.org/10.1155/2020/8834699","pdf_url":"https://downloads.hindawi.com/journals/cin/2020/8834699.pdf","source":{"id":"https://openalex.org/S72372694","display_name":"Computational Intelligence and Neuroscience","issn_l":"1687-5265","issn":["1687-5265","1687-5273"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319869","host_organization_name":"Hindawi Publishing Corporation","host_organization_lineage":["https://openalex.org/P4310319869"],"host_organization_lineage_names":["Hindawi Publishing Corporation"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Intelligence and Neuroscience","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G258723246","display_name":"\u5d4c\u5165\u6027\u89c6\u89d2\u4e0b\u5de5\u4e1a\u805a\u96c6\u533a\u751f\u6001\u98ce\u9669\u4ea4\u53c9\u4f20\u67d3\u673a\u5236\u53ca\u963b\u65ad\u7b56\u7565\u7814\u7a76","funder_award_id":"71503180","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6126550839","display_name":null,"funder_award_id":"20YJA630042","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6593019086","display_name":null,"funder_award_id":"20YJA630042","funder_id":"https://openalex.org/F4320321106","funder_display_name":"Ministry of Education of the People's Republic of China"},{"id":"https://openalex.org/G7858034320","display_name":null,"funder_award_id":"71503180","funder_id":"https://openalex.org/F4320321106","funder_display_name":"Ministry of Education of the People's Republic of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321106","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3091525140.pdf","grobid_xml":"https://content.openalex.org/works/W3091525140.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W842171740","https://openalex.org/W1600830215","https://openalex.org/W1964176024","https://openalex.org/W1968840994","https://openalex.org/W1989272293","https://openalex.org/W2031820816","https://openalex.org/W2044568947","https://openalex.org/W2062087947","https://openalex.org/W2064675550","https://openalex.org/W2065753743","https://openalex.org/W2076485554","https://openalex.org/W2110309989","https://openalex.org/W2152796353","https://openalex.org/W2159326981","https://openalex.org/W2282992258","https://openalex.org/W2529846738","https://openalex.org/W2553839055","https://openalex.org/W2565536624","https://openalex.org/W2566512888","https://openalex.org/W2572287399","https://openalex.org/W2617137613","https://openalex.org/W2736216772","https://openalex.org/W2789364533","https://openalex.org/W2800233837","https://openalex.org/W2802436364","https://openalex.org/W2804806498","https://openalex.org/W2963832956"],"related_works":["https://openalex.org/W2015747722","https://openalex.org/W2362050182","https://openalex.org/W2382418233","https://openalex.org/W2369897927","https://openalex.org/W3031731056","https://openalex.org/W4327918537","https://openalex.org/W4293167957","https://openalex.org/W2361035307","https://openalex.org/W2380455807","https://openalex.org/W3123974110"],"abstract_inverted_index":{"Air":[0],"pollutant":[1,99,168,217,232],"concentration":[2,49,100,157,169,218,233],"forecasting":[3],"is":[4,88,101,125,202],"an":[5],"effective":[6],"way":[7],"which":[8],"protects":[9],"health":[10],"of":[11,17,51,79,95,171,184,198,213],"the":[12,15,18,47,64,86,91,96,121,132,141,166,181,185,194,199,205],"public":[13],"by":[14,32,70,73,106,136],"warning":[16],"harmful":[19],"air":[20,98,155,231],"contaminants.":[21],"In":[22],"this":[23,149,221],"study,":[24],"a":[25,77,82,108,115,210],"hybrid":[26,206],"prediction":[27,50,87,123,143,201,207,225],"model":[28,124,200,208,222],"has":[29,209,223],"been":[30],"established":[31],"using":[33,107],"information":[34,74],"gain,":[35,75],"wavelet":[36,109],"decomposition":[37,110],"transform":[38,111],"technique,":[39],"and":[40,44,59,76,112,131,165,175,227],"LSTM":[41,122],"neural":[42],"network,":[43],"applied":[45],"to":[46,139,163,179],"daily":[48,97],"atmospheric":[52,167,216],"pollutants":[53],"(PM2.5,":[54],"PM10,":[55],"SO2,":[56],"NO2,":[57],"O3,":[58],"CO)":[60],"in":[61,148,159],"Beijing.":[62],"First,":[63],"collected":[65],"raw":[66],"data":[67,118,129,146,158,170,186],"are":[68,134],"selected":[69],"feature":[71],"selection":[72],"set":[78,187],"factors":[80],"having":[81],"strong":[83],"correlation":[84],"with":[85,127],"obtained.":[89],"Then,":[90],"historical":[92],"time":[93],"series":[94],"decomposed":[102],"into":[103,114],"different":[104],"frequencies":[105],"recombined":[113],"high-dimensional":[116,128],"training":[117],"set.":[119],"Finally,":[120],"trained":[126],"sets,":[130],"parameters":[133],"adjusted":[135],"repeated":[137],"tests":[138],"obtain":[140],"optimal":[142],"model.":[144,189],"The":[145,190],"used":[147,178],"study":[150],"were":[151,177],"derived":[152],"from":[153,161],"six":[154],"pollution":[156],"Beijing":[160,172],"1/1/2014":[162],"31/12/2016,":[164],"between":[173],"1/1/2017":[174],"31/12/2017":[176],"test":[180,188],"predictive":[182],"ability":[183],"results":[191],"show":[192],"that":[193],"evaluation":[195],"index":[196],"MAPE":[197],"7.45%.":[203],"Therefore,":[204],"higher":[211,224],"value":[212],"application":[214],"for":[215,229],"prediction,":[219],"because":[220],"accuracy":[226],"stability":[228],"future":[230],"prediction.":[234]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":4}],"updated_date":"2026-06-06T09:05:17.133730","created_date":"2025-10-10T00:00:00"}