{"id":"https://openalex.org/W4385575861","doi":"https://doi.org/10.3233/jifs-224543","title":"Blizzard prediction in antarctic meteorological data using deep learning techniques","display_name":"Blizzard prediction in antarctic meteorological data using deep learning techniques","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385575861","doi":"https://doi.org/10.3233/jifs-224543"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-224543","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-224543","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"},"type":"article","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/A5017382375","display_name":"Vincent Samy","orcid":"https://orcid.org/0000-0002-5352-3118"},"institutions":[{"id":"https://openalex.org/I106814784","display_name":"National Centre for Antarctic and Ocean Research","ror":"https://ror.org/05af1fm66","country_code":"IN","type":"government","lineage":["https://openalex.org/I106814784","https://openalex.org/I4210100914"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"V.S. Samy","raw_affiliation_strings":["National Centre for Polar and Ocean Research, Goa, India"],"affiliations":[{"raw_affiliation_string":"National Centre for Polar and Ocean Research, Goa, India","institution_ids":["https://openalex.org/I106814784"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080105717","display_name":"Veena Thenkanidiyoor","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109276","display_name":"National Institute of Technology Goa","ror":"https://ror.org/01vmfpj79","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210109276"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Veena Thenkanidiyoor","raw_affiliation_strings":["National Institute of Technology, Goa, India","E-mail:"],"affiliations":[{"raw_affiliation_string":"National Institute of Technology, Goa, India","institution_ids":["https://openalex.org/I4210109276"]},{"raw_affiliation_string":"E-mail:","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017382375"],"corresponding_institution_ids":["https://openalex.org/I106814784"],"apc_list":null,"apc_paid":null,"fwci":0.2199,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53828726,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"45","issue":"4","first_page":"6797","last_page":"6812"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9995999932289124,"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":0.9995999932289124,"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/T10644","display_name":"Cryospheric studies and observations","score":0.9950000047683716,"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":0.9947999715805054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.8207160234451294},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7236906290054321},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6732791662216187},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5931196212768555},{"id":"https://openalex.org/keywords/weather-prediction","display_name":"Weather prediction","score":0.5771937370300293},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5725918412208557},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5238428115844727},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5224108695983887},{"id":"https://openalex.org/keywords/atmosphere","display_name":"Atmosphere (unit)","score":0.46272531151771545},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.4617272913455963},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.4311135411262512},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.34433966875076294},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.2753698229789734}],"concepts":[{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.8207160234451294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7236906290054321},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6732791662216187},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5931196212768555},{"id":"https://openalex.org/C2987469573","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather prediction","level":2,"score":0.5771937370300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5725918412208557},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5238428115844727},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5224108695983887},{"id":"https://openalex.org/C65440619","wikidata":"https://www.wikidata.org/wiki/Q177974","display_name":"Atmosphere (unit)","level":2,"score":0.46272531151771545},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.4617272913455963},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.4311135411262512},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.34433966875076294},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.2753698229789734},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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":1,"locations":[{"id":"doi:10.3233/jifs-224543","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-224543","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1964298122","https://openalex.org/W1966066780","https://openalex.org/W1966851414","https://openalex.org/W2014793301","https://openalex.org/W2017369899","https://openalex.org/W2040519605","https://openalex.org/W2056950946","https://openalex.org/W2075986035","https://openalex.org/W2175324911","https://openalex.org/W2290960045","https://openalex.org/W2809710708","https://openalex.org/W2888798257","https://openalex.org/W2897738539","https://openalex.org/W2916433613","https://openalex.org/W3003709258","https://openalex.org/W3048374358","https://openalex.org/W3080366021","https://openalex.org/W3099145603","https://openalex.org/W3128661782","https://openalex.org/W3157531513","https://openalex.org/W3191680402","https://openalex.org/W3198891833","https://openalex.org/W4296770014"],"related_works":["https://openalex.org/W2030498341","https://openalex.org/W2348840575","https://openalex.org/W3122841410","https://openalex.org/W2046205453","https://openalex.org/W2770017172","https://openalex.org/W89977309","https://openalex.org/W2183901182","https://openalex.org/W2030930882","https://openalex.org/W2204614510","https://openalex.org/W3005618458"],"abstract_inverted_index":{"Due":[0],"to":[1,39,57,75,89,129,139,142],"the":[2,6,9,28,32,77,111,124,131,150,169],"unpredictable":[3],"nature":[4],"of":[5,11,30,55,79,85,149,161],"weather":[7,15,99,106,144],"and":[8,21,27,50,72,108,135,155,164,176],"complexity":[10,29],"atmospheric":[12],"movement,":[13],"extreme":[14,98],"has":[16],"always":[17],"been":[18],"a":[19,41,91],"significant":[20],"challenging":[22],"meteorological":[23],"concern.":[24],"Meteorological":[25],"problems":[26],"how":[31],"atmosphere":[33],"moves":[34],"have":[35],"made":[36],"it":[37,141],"necessary":[38],"find":[40],"technological":[42],"solution.":[43],"Deep":[44],"learning":[45],"techniques":[46],"can":[47],"automatically":[48],"learn":[49],"train":[51],"from":[52,123],"vast":[53],"quantities":[54],"data":[56],"provide":[58],"enhanced":[59],"feature":[60],"expression.":[61],"This":[62],"is":[63,88,152],"frequently":[64],"used":[65,128],"in":[66],"computer":[67],"vision,":[68],"natural":[69],"language":[70],"processing,":[71],"other":[73],"domains":[74],"enhance":[76],"performance":[78],"numerous":[80],"real-time":[81],"problems.":[82],"The":[83,147],"purpose":[84],"this":[86],"research":[87],"propose":[90],"deep":[92,113],"learning-based":[93,114],"approach":[94],"for":[95],"effectively":[96],"predicting":[97],"events":[100],"such":[101,173],"as":[102,174],"blizzards.":[103],"To":[104],"recognize":[105],"patterns":[107],"forecast":[109],"blizzards,":[110],"proposed":[112,132],"method":[115],"primarily":[116],"employs":[117],"RNN":[118],"with":[119],"LSTM.":[120],"Real-time":[121],"datasets":[122],"Polar":[125],"Regions":[126],"were":[127,137],"test":[130],"approach\u2019s":[133],"accuracy,":[134],"tests":[136],"conducted":[138],"compare":[140],"existing":[143],"forecasting":[145],"models.":[146],"accuracy":[148],"model":[151],"49.60%":[153],"(univariate)":[154],"55.19%":[156],"(bivariate)":[157],"using":[158],"bivariate":[159],"attributes":[160],"wind":[162],"speed":[163],"air":[165],"pressure":[166],"based":[167],"on":[168],"calculated":[170],"RMSE":[171],"values":[172],"0.0023":[175],"0.0021.":[177]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}