{"id":"https://openalex.org/W4407706009","doi":"https://doi.org/10.1186/s40537-025-01079-9","title":"Deep learning and statistical approaches for area-based PM2.5 forecasting in Hat Yai, Thailand","display_name":"Deep learning and statistical approaches for area-based PM2.5 forecasting in Hat Yai, Thailand","publication_year":2025,"publication_date":"2025-02-18","ids":{"openalex":"https://openalex.org/W4407706009","doi":"https://doi.org/10.1186/s40537-025-01079-9"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01079-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01079-9","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01079-9","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01079-9","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003545345","display_name":"Kasikrit Damkliang","orcid":"https://orcid.org/0000-0002-5342-7302"},"institutions":[{"id":"https://openalex.org/I131868736","display_name":"Prince of Songkla University","ror":"https://ror.org/0575ycz84","country_code":"TH","type":"education","lineage":["https://openalex.org/I131868736"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Kasikrit Damkliang","raw_affiliation_strings":["Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, 90110, Songkhla, Thailand"],"affiliations":[{"raw_affiliation_string":"Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, 90110, Songkhla, Thailand","institution_ids":["https://openalex.org/I131868736"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081251261","display_name":"Jularat Chumnaul","orcid":"https://orcid.org/0000-0001-5719-5513"},"institutions":[{"id":"https://openalex.org/I131868736","display_name":"Prince of Songkla University","ror":"https://ror.org/0575ycz84","country_code":"TH","type":"education","lineage":["https://openalex.org/I131868736"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Jularat Chumnaul","raw_affiliation_strings":["Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, 90110, Songkhla, Thailand"],"affiliations":[{"raw_affiliation_string":"Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, 90110, Songkhla, Thailand","institution_ids":["https://openalex.org/I131868736"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003545345"],"corresponding_institution_ids":["https://openalex.org/I131868736"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":6.1647,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.96220183,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"12","issue":"1","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":0.9998000264167786,"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":0.9998000264167786,"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/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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":0.9909999966621399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6259087324142456},{"id":"https://openalex.org/keywords/computational-science-and-engineering","display_name":"Computational Science and Engineering","score":0.42541268467903137},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3979480266571045},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36262041330337524},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33286845684051514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6259087324142456},{"id":"https://openalex.org/C68597687","wikidata":"https://www.wikidata.org/wiki/Q362601","display_name":"Computational Science and Engineering","level":2,"score":0.42541268467903137},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3979480266571045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36262041330337524},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33286845684051514}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01079-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01079-9","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01079-9","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:59607dd2f10e4e70be7bf335273a6801","is_oa":true,"landing_page_url":"https://doaj.org/article/59607dd2f10e4e70be7bf335273a6801","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 12, Iss 1, Pp 1-24 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01079-9","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01079-9","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01079-9","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"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-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323677","display_name":"Kementerian Sumber Asli dan Alam Sekitar","ror":"https://ror.org/037yznf56"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4407706009.pdf"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2122902902","https://openalex.org/W2140190241","https://openalex.org/W2181523240","https://openalex.org/W2789849108","https://openalex.org/W2937450972","https://openalex.org/W2963155035","https://openalex.org/W2995296280","https://openalex.org/W3003257820","https://openalex.org/W3092684057","https://openalex.org/W3109157657","https://openalex.org/W3116101600","https://openalex.org/W3128149900","https://openalex.org/W3152861131","https://openalex.org/W3157628178","https://openalex.org/W3174719730","https://openalex.org/W3199885704","https://openalex.org/W3200340169","https://openalex.org/W4200026345","https://openalex.org/W4236620421","https://openalex.org/W4285817555","https://openalex.org/W4319460583","https://openalex.org/W4321385933","https://openalex.org/W4322499802","https://openalex.org/W4377041688","https://openalex.org/W4379209530","https://openalex.org/W4379470112","https://openalex.org/W4383503610","https://openalex.org/W4385430829","https://openalex.org/W6923622614"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"PM2.5":[0,20,103],"pollution":[1],"poses":[2],"a":[3,76,89,95,135,141],"significant":[4],"environmental":[5],"and":[6,47,56,81,94,112,118,140],"health":[7],"concern":[8],"across":[9],"Southeast":[10],"Asia,":[11],"including":[12],"Thailand.":[13,166],"This":[14],"study":[15],"aims":[16],"to":[17,40],"forecast":[18],"area-based":[19],"concentrations":[21],"in":[22,164],"Hat":[23,34],"Yai":[24,35],"city,":[25],"Songkhla":[26],"province,":[27],"using":[28,64,123],"daily":[29],"data":[30,124],"collected":[31],"from":[32,38,125],"the":[33,68,71,85,131,151],"monitoring":[36],"station":[37],"2013":[39],"2023.":[41],"To":[42],"achieve":[43],"this,":[44],"we":[45],"developed":[46],"evaluated":[48],"forecasting":[49],"models":[50],"utilizing":[51],"both":[52],"Deep":[53],"Learning":[54,58],"(DL)":[55],"Machine":[57],"(ML)":[59],"techniques,":[60],"with":[61,75],"performance":[62],"assessed":[63],"statistical":[65],"tools.":[66],"Among":[67],"tested":[69],"models,":[70],"ConvLSTM1D-BiLSTM":[72],"model,":[73],"optimized":[74],"7-input":[77],"window,":[78,80],"1-output":[79],"6":[82],"strides,":[83],"demonstrated":[84],"highest":[86],"effectiveness,":[87,133],"achieving":[88],"mean":[90,96,136,142],"MAE":[91,137],"of":[92,100,115,138,146,153],"2.05":[93],"R":[97,143],"$$^2$$":[98,144],"score":[99,145],"0.68.":[101],"In":[102],"level":[104],"classification,":[105],"this":[106],"model":[107],"attained":[108],"macro-average":[109],"accuracy,":[110],"sensitivity,":[111],"F1":[113],"scores":[114],"0.86,":[116],"0.80,":[117,119],"respectively.":[120],"External":[121],"validation":[122],"four":[126],"nearby":[127],"stations":[128,163],"further":[129],"confirmed":[130],"model\u2019s":[132],"yielding":[134],"1.38":[139],"0.90.":[147],"These":[148],"results":[149],"underscore":[150],"robustness":[152],"our":[154],"approach,":[155],"supporting":[156],"its":[157],"practical":[158],"application,":[159],"particularly":[160],"for":[161],"local":[162],"Southern":[165]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5}],"updated_date":"2026-04-07T14:57:38.498316","created_date":"2025-10-10T00:00:00"}