{"id":"https://openalex.org/W2932002651","doi":"https://doi.org/10.1109/iccnc.2019.8685584","title":"A Deep Learning-Based Weather Forecast System for Data Volume and Recency Analysis","display_name":"A Deep Learning-Based Weather Forecast System for Data Volume and Recency Analysis","publication_year":2019,"publication_date":"2019-02-01","ids":{"openalex":"https://openalex.org/W2932002651","doi":"https://doi.org/10.1109/iccnc.2019.8685584","mag":"2932002651"},"language":"en","primary_location":{"id":"doi:10.1109/iccnc.2019.8685584","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccnc.2019.8685584","pdf_url":null,"source":{"id":"https://openalex.org/S4306498526","display_name":"2019 International Conference on Computing, Networking and Communications (ICNC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Computing, Networking and Communications (ICNC)","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/A5025498237","display_name":"Jarrett Booz","orcid":null},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jarrett Booz","raw_affiliation_strings":["Towson University, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Towson University, MD, USA","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002139930","display_name":"Wei Yu","orcid":"https://orcid.org/0000-0003-4522-7340"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Yu","raw_affiliation_strings":["Towson University, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Towson University, MD, USA","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025502225","display_name":"Guobin Xu","orcid":"https://orcid.org/0000-0002-9483-5388"},"institutions":[{"id":"https://openalex.org/I152830075","display_name":"Frostburg State University","ror":"https://ror.org/048drzm61","country_code":"US","type":"education","lineage":["https://openalex.org/I152830075"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guobin Xu","raw_affiliation_strings":["Frostburg State University, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Frostburg State University, MD, USA","institution_ids":["https://openalex.org/I152830075"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064877913","display_name":"David Griffith","orcid":"https://orcid.org/0000-0002-7986-1924"},"institutions":[{"id":"https://openalex.org/I1321296531","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416","country_code":"US","type":"funder","lineage":["https://openalex.org/I1321296531","https://openalex.org/I1343035065"]},{"id":"https://openalex.org/I4210124755","display_name":"National Institute of Standards","ror":"https://ror.org/02zftm050","country_code":"EG","type":"facility","lineage":["https://openalex.org/I4210124755"]}],"countries":["EG","US"],"is_corresponding":false,"raw_author_name":"David Griffith","raw_affiliation_strings":["National Institute of Standards and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Standards and Technology","institution_ids":["https://openalex.org/I1321296531","https://openalex.org/I4210124755"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060015284","display_name":"Nada Golmie","orcid":"https://orcid.org/0000-0002-2898-1651"},"institutions":[{"id":"https://openalex.org/I1321296531","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416","country_code":"US","type":"funder","lineage":["https://openalex.org/I1321296531","https://openalex.org/I1343035065"]},{"id":"https://openalex.org/I4210124755","display_name":"National Institute of Standards","ror":"https://ror.org/02zftm050","country_code":"EG","type":"facility","lineage":["https://openalex.org/I4210124755"]}],"countries":["EG","US"],"is_corresponding":false,"raw_author_name":"Nada Golmie","raw_affiliation_strings":["National Institute of Standards and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Standards and Technology","institution_ids":["https://openalex.org/I1321296531","https://openalex.org/I4210124755"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"697","last_page":"701"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9945999979972839,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9945999979972839,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9915000200271606,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9853000044822693,"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/computer-science","display_name":"Computer science","score":0.6547269821166992},{"id":"https://openalex.org/keywords/weather-prediction","display_name":"Weather prediction","score":0.6209664344787598},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.620201587677002},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5810170769691467},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5732192993164062},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.5652108788490295},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.47851574420928955},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4745260775089264},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43116337060928345},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37176409363746643},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.15171408653259277}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6547269821166992},{"id":"https://openalex.org/C2987469573","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather prediction","level":2,"score":0.6209664344787598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.620201587677002},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5810170769691467},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5732192993164062},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.5652108788490295},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.47851574420928955},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4745260775089264},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43116337060928345},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37176409363746643},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.15171408653259277},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccnc.2019.8685584","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccnc.2019.8685584","pdf_url":null,"source":{"id":"https://openalex.org/S4306498526","display_name":"2019 International Conference on Computing, Networking and Communications (ICNC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Computing, Networking and Communications (ICNC)","raw_type":"proceedings-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/W1632156163","https://openalex.org/W1969845070","https://openalex.org/W1984020445","https://openalex.org/W2001666598","https://openalex.org/W2002521620","https://openalex.org/W2007272376","https://openalex.org/W2020960725","https://openalex.org/W2068181924","https://openalex.org/W2076063813","https://openalex.org/W2102148524","https://openalex.org/W2342249984","https://openalex.org/W2562619077","https://openalex.org/W2596636257","https://openalex.org/W2768475350","https://openalex.org/W2790400697","https://openalex.org/W2800017313","https://openalex.org/W2804274048","https://openalex.org/W2919115771","https://openalex.org/W2939342150","https://openalex.org/W2963042536","https://openalex.org/W3022384591","https://openalex.org/W6776441365","https://openalex.org/W6910546390"],"related_works":["https://openalex.org/W4377970672","https://openalex.org/W4391877217","https://openalex.org/W2970320467","https://openalex.org/W4399449843","https://openalex.org/W3127901020","https://openalex.org/W4210762563","https://openalex.org/W2998567488","https://openalex.org/W3021464272","https://openalex.org/W4389919864","https://openalex.org/W2348248161"],"abstract_inverted_index":{"Accurate":[0],"weather":[1,24,31,43,53,67,140],"forecast":[2,54],"is":[3,148,201,218],"important":[4],"to":[5,74,138,150,193,204,221],"our":[6,133],"daily":[7],"life":[8],"and":[9,13,41,56,60,89,116,127,215],"have":[10,143,165],"both":[11],"economic":[12],"environment":[14],"impact.":[15],"Through":[16,130],"physical":[17],"atmospheric":[18],"models,":[19],"a":[20,50,65,71,155,166],"short":[21],"period":[22],"time":[23],"can":[25,36],"be":[26,37],"accurately":[27],"forecasted.":[28],"To":[29],"provide":[30],"forecast,":[32],"machines":[33],"learning":[34,77,81],"techniques":[35],"used":[38],"for":[39,234],"understanding":[40],"analyzing":[42],"patterns.":[44],"In":[45],"this":[46,189],"paper,":[47],"we":[48,96,105,142],"propose":[49],"deep":[51,80],"learning-based":[52],"system":[55],"conduct":[57],"data":[58,68,117,128,141,147,162],"volume":[59],"recency":[61,159],"analysis":[62],"by":[63,209],"utilizing":[64],"real-world":[66],"set":[69],"as":[70],"case":[72],"study":[73],"demonstrate":[75],"the":[76,85,98,103,110,113,121,124,139,152,161,171,174,195,210,224,231,235],"ability":[78],"of":[79,154,160,173,213],"model.":[82,157,176],"By":[83],"using":[84],"Python":[86],"Keras":[87],"library":[88,91],"Pandas":[90],"<sup":[92,177],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[93,178],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[94,179],",":[95],"implement":[97],"proposed":[99],"system.":[100],"Based":[101],"on":[102,170],"system,":[104],"find":[106],"out":[107],"not":[108,164,202],"only":[109],"relationship":[111,122],"between":[112,123],"prediction":[114,125],"accuracy":[115,126,153,172],"volume,":[118],"but":[119],"also":[120],"recency.":[129],"extensive":[131],"evaluations,":[132],"results":[134],"show":[135],"that":[136,223],"according":[137],"been":[144],"using,":[145],"more":[146],"beneficial":[149],"increasing":[151],"trained":[156,175],"The":[158],"does":[163],"consistently":[167],"significant":[168],"impact":[169],"Certain":[180],"commercial":[181],"equipment,":[182],"instruments,":[183],"or":[184,207,226],"materials":[185,225],"are":[186,229],"identified":[187,228],"in":[188,191],"paper":[190],"order":[192],"specify":[194],"experimental":[196],"procedure":[197],"adequately.":[198],"Such":[199],"identification":[200],"intended":[203,220],"imply":[205,222],"recommendation":[206],"endorsement":[208],"National":[211],"Institute":[212],"Standards":[214],"Technology,":[216],"nor":[217],"it":[219],"equipment":[227],"necessarily":[230],"best":[232],"available":[233],"purpose.":[236]},"counts_by_year":[{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
