{"id":"https://openalex.org/W3197341793","doi":"https://doi.org/10.1109/icufn49451.2021.9528816","title":"A Study on Rainfall Prediction based on Meteorological Time Series","display_name":"A Study on Rainfall Prediction based on Meteorological Time Series","publication_year":2021,"publication_date":"2021-08-17","ids":{"openalex":"https://openalex.org/W3197341793","doi":"https://doi.org/10.1109/icufn49451.2021.9528816","mag":"3197341793"},"language":"en","primary_location":{"id":"doi:10.1109/icufn49451.2021.9528816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn49451.2021.9528816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","raw_type":"proceedings-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/A5005511954","display_name":"KangWoon Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"KangWoon Hong","raw_affiliation_strings":["Electronics and Telecommunications Research Institute, Daejeon, Korea"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute, Daejeon, Korea","institution_ids":["https://openalex.org/I142401562"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065556460","display_name":"Taegyu Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I142401562","display_name":"Electronics and Telecommunications Research Institute","ror":"https://ror.org/03ysstz10","country_code":"KR","type":"facility","lineage":["https://openalex.org/I142401562","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Taegyu Kang","raw_affiliation_strings":["Electronics and Telecommunications Research Institute, Daejeon, Korea"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunications Research Institute, Daejeon, Korea","institution_ids":["https://openalex.org/I142401562"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5005511954"],"corresponding_institution_ids":["https://openalex.org/I142401562"],"apc_list":null,"apc_paid":null,"fwci":0.2684,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.51397218,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"30","issue":null,"first_page":"302","last_page":"304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9660999774932861,"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":0.9660999774932861,"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/T10930","display_name":"Flood Risk Assessment and Management","score":0.9545999765396118,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9096999764442444,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/univariate","display_name":"Univariate","score":0.8206392526626587},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.7200185060501099},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6657652258872986},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6116443276405334},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6065646409988403},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5915195345878601},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5588906407356262},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5418819189071655},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5302379131317139},{"id":"https://openalex.org/keywords/weather-prediction","display_name":"Weather prediction","score":0.4656091034412384},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4601294696331024},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41437989473342896},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.2703445553779602},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07805472612380981}],"concepts":[{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.8206392526626587},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.7200185060501099},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6657652258872986},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6116443276405334},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6065646409988403},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5915195345878601},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5588906407356262},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5418819189071655},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5302379131317139},{"id":"https://openalex.org/C2987469573","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather prediction","level":2,"score":0.4656091034412384},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4601294696331024},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41437989473342896},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.2703445553779602},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07805472612380981},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icufn49451.2021.9528816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn49451.2021.9528816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G5053614325","display_name":null,"funder_award_id":"1711101951","funder_id":"https://openalex.org/F4320325370","funder_display_name":"National Research Council of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320325370","display_name":"National Research Council of Science and Technology","ror":"https://ror.org/058rymf81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1947481528","https://openalex.org/W2625614184","https://openalex.org/W2951183276","https://openalex.org/W2963866658","https://openalex.org/W3009216045","https://openalex.org/W6628877408","https://openalex.org/W6739112683"],"related_works":["https://openalex.org/W2049578243","https://openalex.org/W1828158523","https://openalex.org/W2122079181","https://openalex.org/W2000145235","https://openalex.org/W1985848810","https://openalex.org/W2889939530","https://openalex.org/W2399195672","https://openalex.org/W3121881699","https://openalex.org/W2748838164","https://openalex.org/W2066015000"],"abstract_inverted_index":{"This":[0],"study":[1],"aims":[2],"to":[3],"present":[4],"the":[5,8,14,20,27,42,54,58,68,80,84],"results":[6,28],"of":[7,29,45,71],"research":[9],"and":[10,36],"development":[11],"project":[12],"on":[13],"urban":[15],"inundation":[16],"prediction":[17,31],"technology":[18],"during":[19],"heavy":[21],"rain":[22],"period.":[23],"In":[24,41,67],"this":[25],"study,":[26],"rainfall":[30],"using":[32],"heterogeneous":[33],"weather":[34],"data":[35],"machine":[37],"learning":[38],"are":[39],"presented.":[40],"predictive":[43,69],"analysis":[44,70],"univariate":[46],"time":[47,73],"series":[48,74],"data,":[49,75],"it":[50,76],"was":[51,77],"confirmed":[52,78],"that":[53,79],"CNN-LSTM":[55],"model":[56,82],"showed":[57,83],"best":[59,85],"performance":[60,86],"among":[61,87],"several":[62,88],"deep":[63,89],"neural":[64,90],"network":[65,91],"models.":[66,92],"multivariate":[72],"ConvLSTM":[81]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
