{"id":"https://openalex.org/W3189793971","doi":"https://doi.org/10.3390/rs13153004","title":"A Novel Modeling Strategy of Weighted Mean Temperature in China Using RNN and LSTM","display_name":"A Novel Modeling Strategy of Weighted Mean Temperature in China Using RNN and LSTM","publication_year":2021,"publication_date":"2021-07-30","ids":{"openalex":"https://openalex.org/W3189793971","doi":"https://doi.org/10.3390/rs13153004","mag":"3189793971"},"language":"en","primary_location":{"id":"doi:10.3390/rs13153004","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13153004","pdf_url":"https://www.mdpi.com/2072-4292/13/15/3004/pdf?version=1627991169","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/13/15/3004/pdf?version=1627991169","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001464661","display_name":"Wenliang Gao","orcid":"https://orcid.org/0000-0002-2891-0693"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenliang Gao","raw_affiliation_strings":["School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"],"affiliations":[{"raw_affiliation_string":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China","institution_ids":["https://openalex.org/I25757504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100742507","display_name":"Jingxiang Gao","orcid":"https://orcid.org/0000-0001-7495-7022"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingxiang Gao","raw_affiliation_strings":["School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"],"affiliations":[{"raw_affiliation_string":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China","institution_ids":["https://openalex.org/I25757504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101817853","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-3552-3579"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liu Yang","raw_affiliation_strings":["School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"],"affiliations":[{"raw_affiliation_string":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China","institution_ids":["https://openalex.org/I25757504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100734753","display_name":"Mingjun Wang","orcid":"https://orcid.org/0000-0001-9648-4123"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingjun Wang","raw_affiliation_strings":["School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"],"affiliations":[{"raw_affiliation_string":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China","institution_ids":["https://openalex.org/I25757504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070359332","display_name":"Wenhao Yao","orcid":"https://orcid.org/0000-0003-2310-1421"},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhao Yao","raw_affiliation_strings":["School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"],"affiliations":[{"raw_affiliation_string":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China","institution_ids":["https://openalex.org/I25757504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100742507"],"corresponding_institution_ids":["https://openalex.org/I25757504"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":25.1786,"has_fulltext":true,"cited_by_count":37,"citation_normalized_percentile":{"value":0.99122986,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"13","issue":"15","first_page":"3004","last_page":"3004"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10655","display_name":"GNSS positioning and interference","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10655","display_name":"GNSS positioning and interference","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10159","display_name":"Ionosphere and magnetosphere dynamics","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.7998353242874146},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7726129293441772},{"id":"https://openalex.org/keywords/radiosonde","display_name":"Radiosonde","score":0.7036765217781067},{"id":"https://openalex.org/keywords/mean-radiant-temperature","display_name":"Mean radiant temperature","score":0.501103401184082},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4974088966846466},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4474034607410431},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4278792142868042},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41770613193511963},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.3660805821418762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36154547333717346},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3447079360485077},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.3205244541168213},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2919768691062927},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08975893259048462}],"concepts":[{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.7998353242874146},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7726129293441772},{"id":"https://openalex.org/C11999413","wikidata":"https://www.wikidata.org/wiki/Q852817","display_name":"Radiosonde","level":2,"score":0.7036765217781067},{"id":"https://openalex.org/C42683663","wikidata":"https://www.wikidata.org/wiki/Q3517834","display_name":"Mean radiant temperature","level":3,"score":0.501103401184082},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4974088966846466},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4474034607410431},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4278792142868042},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41770613193511963},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.3660805821418762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36154547333717346},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3447079360485077},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3205244541168213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2919768691062927},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08975893259048462},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs13153004","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13153004","pdf_url":"https://www.mdpi.com/2072-4292/13/15/3004/pdf?version=1627991169","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8c393a27868d45c48d91dbf94e11caa6","is_oa":true,"landing_page_url":"https://doaj.org/article/8c393a27868d45c48d91dbf94e11caa6","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 15, p 3004 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/15/3004/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13153004","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Remote Sensing; Volume 13; Issue 15; Pages: 3004","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13153004","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13153004","pdf_url":"https://www.mdpi.com/2072-4292/13/15/3004/pdf?version=1627991169","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6"}],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3193465348","display_name":null,"funder_award_id":"41874006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6817330045","display_name":null,"funder_award_id":"41974026","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3189793971.pdf","grobid_xml":"https://content.openalex.org/works/W3189793971.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1974536365","https://openalex.org/W2018874360","https://openalex.org/W2020097894","https://openalex.org/W2047158459","https://openalex.org/W2064675550","https://openalex.org/W2081182187","https://openalex.org/W2099473264","https://openalex.org/W2112306707","https://openalex.org/W2119486063","https://openalex.org/W2151536681","https://openalex.org/W2174616381","https://openalex.org/W2284447156","https://openalex.org/W2322308136","https://openalex.org/W2474071962","https://openalex.org/W2605301990","https://openalex.org/W2619995677","https://openalex.org/W2746245863","https://openalex.org/W2755346628","https://openalex.org/W2782180140","https://openalex.org/W2793913504","https://openalex.org/W2803340376","https://openalex.org/W2803789078","https://openalex.org/W2898796705","https://openalex.org/W2905485021","https://openalex.org/W2921004868","https://openalex.org/W2956497647","https://openalex.org/W2968008393","https://openalex.org/W2970205641","https://openalex.org/W3010715572","https://openalex.org/W3021540383","https://openalex.org/W3043953499","https://openalex.org/W3121041782","https://openalex.org/W3133675722"],"related_works":["https://openalex.org/W2043100303","https://openalex.org/W2898002340","https://openalex.org/W1661883744","https://openalex.org/W4200011432","https://openalex.org/W313882189","https://openalex.org/W2920062132","https://openalex.org/W1980612756","https://openalex.org/W333611740","https://openalex.org/W2027412012","https://openalex.org/W2964006806"],"abstract_inverted_index":{"In":[0,40,165,218],"the":[1,8,18,22,90,106,117,133,147,151,155,178,183,187,207,220],"meteorology":[2],"of":[3,20,132,186],"Global":[4,108],"Navigation":[5],"Satellite":[6],"System,":[7],"weighted":[9,65,91,188,230],"mean":[10,66,92,128,189,231],"temperature":[11,67,87,190,232],"(Tm)":[12],"is":[13],"a":[14,63,226],"key":[15],"parameter":[16],"in":[17,36,100,174,213,216,229],"process":[19],"converting":[21],"zenith":[23],"wetness":[24],"delay":[25],"into":[26],"precipitable":[27],"water":[28,37],"vapor,":[29],"and":[30,52,84,101,119,135,141,162,176,201,222],"it":[31],"plays":[32],"an":[33],"important":[34],"role":[35],"vapor":[38],"monitoring.":[39],"this":[41],"research,":[42],"two":[43],"deep":[44],"learning":[45],"algorithms,":[46],"namely,":[47],"recurrent":[48],"neural":[49,56],"network":[50,57],"(RNN)":[51],"long":[53],"short-term":[54],"memory":[55,76],"(LSTM),":[58],"were":[59,138,198],"used":[60,95,177],"to":[61,88,113,115,181,193],"build":[62],"high-precision":[64],"model":[68,79,118,124,180,208],"for":[69,123],"China":[70,103,175],"using":[71],"their":[72],"excellent":[73],"time":[74],"series":[75],"capability.":[77],"The":[78,126,195],"needs":[80],"site":[81],"location":[82],"information":[83],"measured":[85],"surface":[86],"predict":[89],"temperature.":[93],"We":[94],"data":[96,120],"from":[97,111,121,191],"118":[98],"stations":[99,173],"around":[102],"provided":[104],"by":[105,150,159],"Integrated":[107],"Radiosonde":[109],"Archive":[110],"2010":[112,192],"2015":[114],"train":[116],"2016":[122],"testing.":[125],"root":[127],"square":[129],"error":[130],"(RMSE)":[131],"RNN_Tm":[134,221],"LSTM_Tm":[136,223],"models":[137,224],"3.01":[139],"K":[140,200],"2.89":[142],"K,":[143,203],"respectively.":[144],"Compared":[145],"with":[146],"values":[148,197],"calculated":[149],"empirical":[152],"GPT3":[153],"model,":[154],"accuracy":[156],"was":[157],"improved":[158],"31.1%":[160],"(RNN_Tm)":[161],"33.9%":[163],"(LSTM_Tm).":[164],"addition,":[166],"we":[167],"selected":[168],"another":[169],"10":[170],"evenly":[171],"distributed":[172],"constructed":[179],"test":[182],"prediction":[184],"capability":[185],"2016.":[194],"RMSE":[196],"2.95":[199],"2.86":[202],"which":[204],"proved":[205],"that":[206],"also":[209],"exhibits":[210],"high":[211],"generalization":[212],"non-modeling":[214],"sites":[215],"China.":[217],"general,":[219],"have":[225],"good":[227],"performance":[228],"prediction.":[233]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}