{"id":"https://openalex.org/W3040433874","doi":"https://doi.org/10.1145/3406601.3406625","title":"The Impact of Data Imputation and Feature Extraction on PM2.5 Forecasting Performance in Bangkok Using Long Short-Term Memory Neural Networks","display_name":"The Impact of Data Imputation and Feature Extraction on PM2.5 Forecasting Performance in Bangkok Using Long Short-Term Memory Neural Networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3040433874","doi":"https://doi.org/10.1145/3406601.3406625","mag":"3040433874"},"language":"en","primary_location":{"id":"doi:10.1145/3406601.3406625","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3406601.3406625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th International Conference on Advances in Information Technology","raw_type":"proceedings-article"},"type":"conference-paper","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/A5016836170","display_name":"Panupong Rong-O","orcid":null},"institutions":[{"id":"https://openalex.org/I76920116","display_name":"Srinakharinwirot University","ror":"https://ror.org/04718hx42","country_code":"TH","type":"education","lineage":["https://openalex.org/I76920116"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Panupong Rong-O","raw_affiliation_strings":["Master of Science in Information Technology, Srinakharinwirot University Bangkok, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Master of Science in Information Technology, Srinakharinwirot University Bangkok, Thailand","institution_ids":["https://openalex.org/I76920116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075696505","display_name":"Nuwee Wiwatwattana","orcid":"https://orcid.org/0000-0002-9513-0937"},"institutions":[{"id":"https://openalex.org/I76920116","display_name":"Srinakharinwirot University","ror":"https://ror.org/04718hx42","country_code":"TH","type":"education","lineage":["https://openalex.org/I76920116"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Nuwee Wiwatwattana","raw_affiliation_strings":["Department of Computer Science, Srinakharinwirot University Bangkok, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Srinakharinwirot University Bangkok, Thailand","institution_ids":["https://openalex.org/I76920116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76920116"],"apc_list":null,"apc_paid":null,"fwci":0.3943,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51254985,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"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":0.9961000084877014,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9904999732971191,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/mean-squared-error","display_name":"Mean squared error","score":0.7102088332176208},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6741939783096313},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6650068163871765},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6596625447273254},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5839217901229858},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.551062285900116},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5398930311203003},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5306169986724854},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5199419260025024},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4688476324081421},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.42171183228492737},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38280051946640015},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.3677979111671448},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3414829969406128},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33447587490081787},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1299925148487091}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7102088332176208},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6741939783096313},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6650068163871765},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6596625447273254},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5839217901229858},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.551062285900116},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5398930311203003},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5306169986724854},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5199419260025024},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4688476324081421},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.42171183228492737},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38280051946640015},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3677979111671448},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3414829969406128},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33447587490081787},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1299925148487091},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3406601.3406625","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3406601.3406625","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 11th International Conference on Advances in Information Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7599999904632568,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2289128632","https://openalex.org/W2581215590","https://openalex.org/W2636145106","https://openalex.org/W2740925399","https://openalex.org/W2775056555","https://openalex.org/W2812669263","https://openalex.org/W2891220458","https://openalex.org/W2891727608","https://openalex.org/W2892035816","https://openalex.org/W2898924913","https://openalex.org/W2899186517","https://openalex.org/W2909340263","https://openalex.org/W2909911010","https://openalex.org/W2921835983","https://openalex.org/W2964010366","https://openalex.org/W2984594917","https://openalex.org/W2995015263","https://openalex.org/W3003133065","https://openalex.org/W3003534682","https://openalex.org/W3004417816","https://openalex.org/W3012957873"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"The":[0],"last":[1],"few":[2],"years":[3],"have":[4,19],"seen":[5],"a":[6,90],"dramatic":[7],"increase":[8],"in":[9,13,54,87,96],"PM2.5":[10,33,95],"air":[11],"pollution":[12],"Thailand's":[14],"major":[15],"cities.":[16],"Various":[17],"works":[18],"tried":[20],"to":[21,93,122,137,149,154,159],"develop":[22],"efficient":[23],"Long":[24],"Short-Term":[25],"Memory":[26],"(LSTM)":[27],"deep":[28],"neural":[29],"network":[30],"models":[31,110],"for":[32,79],"concentration":[34],"forecasting.":[35],"However,":[36],"little":[37],"has":[38],"been":[39],"studied":[40],"about":[41],"the":[42,51,74,83,97,104,123,138,161],"impact":[43],"of":[44,163],"data":[45,113],"imputation":[46],"and":[47,67,129],"feature":[48,80],"extraction":[49],"on":[50],"model":[52],"performance":[53,105],"this":[55,58],"context.":[56],"In":[57],"paper,":[59],"we":[60,101],"imputed":[61,112],"missing":[62],"values":[63],"using":[64],"Kalman":[65],"Smoothing":[66],"Linearly":[68],"Weighted":[69],"Moving":[70],"Average.":[71],"We":[72],"utilized":[73],"LSTM":[75,109,145,164],"Autoencoder":[76],"(LSTM":[77],"AE)":[78],"extraction.":[81],"Using":[82],"Chokchai":[84],"Police":[85],"station":[86],"Bangkok":[88],"as":[89],"case":[91],"study":[92],"predict":[94],"next":[98],"24":[99,155],"hours,":[100],"demonstrated":[102],"that":[103],"gain":[106],"from":[107],"training":[108],"with":[111,120,135,144],"is":[114],"more":[115,130],"than":[116,131],"7":[117],"percent":[118,133],"overall":[119,134],"respect":[121,136],"root":[124],"mean":[125,139],"square":[126],"error":[127,141],"(RMSE)":[128],"10":[132],"absolute":[140],"(MAE).":[142],"Improvement":[143],"AE":[146],"varies":[147],"according":[148],"time":[150],"steps.":[151],"Forecasting":[152],"22":[153],"hours":[156],"ahead":[157],"tends":[158],"favor":[160],"use":[162],"AE.":[165]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}