{"id":"https://openalex.org/W3045726380","doi":"https://doi.org/10.1145/3409073.3409097","title":"Data Assimilation by Artificial Neural Network using Conventional Observation for WRF Model","display_name":"Data Assimilation by Artificial Neural Network using Conventional Observation for WRF Model","publication_year":2020,"publication_date":"2020-06-19","ids":{"openalex":"https://openalex.org/W3045726380","doi":"https://doi.org/10.1145/3409073.3409097","mag":"3045726380"},"language":"en","primary_location":{"id":"doi:10.1145/3409073.3409097","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3409073.3409097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","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/A5006993955","display_name":"Shijin Yuan","orcid":"https://orcid.org/0000-0002-8102-3137"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shijin Yuan","raw_affiliation_strings":["School of software engineering, Tongji University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of software engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102768463","display_name":"Bo Shi","orcid":"https://orcid.org/0000-0002-2468-5038"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Shi","raw_affiliation_strings":["School of software engineering, Tongji University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of software engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081341561","display_name":"Bin Mu","orcid":"https://orcid.org/0000-0003-4414-9811"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Mu","raw_affiliation_strings":["School of software engineering, Tongji University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of software engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"62","last_page":"67"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9998999834060669,"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/T10029","display_name":"Climate variability and models","score":0.9980999827384949,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9909999966621399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.8639980554580688},{"id":"https://openalex.org/keywords/data-assimilation","display_name":"Data assimilation","score":0.6804760694503784},{"id":"https://openalex.org/keywords/weather-research-and-forecasting-model","display_name":"Weather Research and Forecasting Model","score":0.6564042568206787},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6377222537994385},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5705825090408325},{"id":"https://openalex.org/keywords/mesoscale-meteorology","display_name":"Mesoscale meteorology","score":0.4161917269229889},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37287822365760803},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29959625005722046},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.1439899504184723},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06847906112670898}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.8639980554580688},{"id":"https://openalex.org/C24552861","wikidata":"https://www.wikidata.org/wiki/Q2670177","display_name":"Data assimilation","level":2,"score":0.6804760694503784},{"id":"https://openalex.org/C133204551","wikidata":"https://www.wikidata.org/wiki/Q838305","display_name":"Weather Research and Forecasting Model","level":2,"score":0.6564042568206787},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6377222537994385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5705825090408325},{"id":"https://openalex.org/C40382383","wikidata":"https://www.wikidata.org/wiki/Q2399824","display_name":"Mesoscale meteorology","level":2,"score":0.4161917269229889},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37287822365760803},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29959625005722046},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.1439899504184723},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06847906112670898}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3409073.3409097","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3409073.3409097","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 5th International Conference on Machine Learning Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5709200773","display_name":null,"funder_award_id":"22120190207","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1979155492","https://openalex.org/W1987308763","https://openalex.org/W2022265298","https://openalex.org/W2109364787","https://openalex.org/W2147119488","https://openalex.org/W2157098139","https://openalex.org/W2804982827","https://openalex.org/W2954554765","https://openalex.org/W3145013517","https://openalex.org/W4244731586"],"related_works":["https://openalex.org/W3139996901","https://openalex.org/W2733927550","https://openalex.org/W3139950680","https://openalex.org/W4327737446","https://openalex.org/W2998858242","https://openalex.org/W2893778714","https://openalex.org/W4381687522","https://openalex.org/W2016133290","https://openalex.org/W2566858736","https://openalex.org/W841036956"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"artificial":[3],"neural":[4],"network(ANN)":[5],"are":[6,53,84,114],"introduced":[7],"to":[8,35,88,142],"data":[9,27,94],"assimilation":[10,28,71,95],"for":[11,70],"WRF":[12,67],"model,":[13],"which":[14],"is":[15,31,46,64,96,123],"a":[16,125],"mesoscale":[17],"complex":[18],"model.":[19,91],"A":[20],"particle":[21,57],"swarm":[22,58],"optimization":[23,59],"optimized":[24],"Multilayer":[25,44],"Perception":[26,45],"(MLP-PSO-DA)":[29],"model":[30,138],"proposed":[32],"in":[33],"order":[34],"emulate":[36],"the":[37,49,90,118,131,136,145],"ensemble":[38],"square":[39],"root":[40],"filter":[41],"(EnSRF)":[42],"analysis.":[43],"employed":[47],"and":[48,82,112,117],"optimal":[50],"parameter":[51],"configurations":[52],"automatic":[54],"obtained":[55],"by":[56],"(PSO)":[60],"algorithm.":[61],"The":[62,73,92,105,128],"MLP-PSO-DA":[63,111,137],"integrated":[65],"with":[66,101],"modeling":[68],"system":[69],"cycle.":[72],"EnSRF":[74,113],"analysis":[75,108],"fields":[76,109],"from":[77],"July":[78],"of":[79,103,110,133],"2004,":[80],"2005":[81],"2006":[83],"taking":[85],"as":[86],"samples":[87],"train":[89],"ANN-based":[93],"conducted":[97],"at":[98],"July,":[99],"2007":[100],"interval":[102],"6h.":[104],"prognostic":[106],"variables":[107],"very":[115],"similar":[116],"difference":[119],"between":[120],"two":[121],"method":[122],"within":[124],"small":[126],"scope.":[127],"results":[129],"prove":[130],"effectiveness":[132],"MLP-PSO-DA.":[134],"Meanwhile,":[135],"has":[139],"great":[140],"advantage":[141],"speed":[143],"up":[144],"DA":[146],"process.":[147]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
