{"id":"https://openalex.org/W3116680594","doi":"https://doi.org/10.1109/gcce50665.2020.9291972","title":"A Pluvial Flood Nowcasting Approach using Knowledge Distillation","display_name":"A Pluvial Flood Nowcasting Approach using Knowledge Distillation","publication_year":2020,"publication_date":"2020-10-13","ids":{"openalex":"https://openalex.org/W3116680594","doi":"https://doi.org/10.1109/gcce50665.2020.9291972","mag":"3116680594"},"language":"en","primary_location":{"id":"doi:10.1109/gcce50665.2020.9291972","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce50665.2020.9291972","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)","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/A5110542635","display_name":"Ryoma Kondo","orcid":"https://orcid.org/0009-0000-1190-6084"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryoma KONDO","raw_affiliation_strings":["Graduate School of Engineering, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068346224","display_name":"Yoshiaki Narusue","orcid":"https://orcid.org/0000-0001-7957-0839"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshiaki NARUSUE","raw_affiliation_strings":["Graduate School of Engineering, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043387979","display_name":"Hiroyuki Morikawa","orcid":"https://orcid.org/0000-0002-8280-9553"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroyuki MORIKAWA","raw_affiliation_strings":["Graduate School of Engineering, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.1004,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52096642,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"70","issue":null,"first_page":"198","last_page":"201"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10930","display_name":"Flood Risk Assessment and Management","score":0.9991999864578247,"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/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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":0.9955000281333923,"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/nowcasting","display_name":"Nowcasting","score":0.8978418111801147},{"id":"https://openalex.org/keywords/pluvial","display_name":"Pluvial","score":0.8161864280700684},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5838750004768372},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5500848293304443},{"id":"https://openalex.org/keywords/flood-myth","display_name":"Flood myth","score":0.5453696250915527},{"id":"https://openalex.org/keywords/precipitation","display_name":"Precipitation","score":0.47683778405189514},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.3717712163925171},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3019287586212158},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.21270102262496948},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09263190627098083}],"concepts":[{"id":"https://openalex.org/C2781013037","wikidata":"https://www.wikidata.org/wiki/Q1433331","display_name":"Nowcasting","level":2,"score":0.8978418111801147},{"id":"https://openalex.org/C2780380513","wikidata":"https://www.wikidata.org/wiki/Q1852115","display_name":"Pluvial","level":2,"score":0.8161864280700684},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5838750004768372},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5500848293304443},{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.5453696250915527},{"id":"https://openalex.org/C107054158","wikidata":"https://www.wikidata.org/wiki/Q25257","display_name":"Precipitation","level":2,"score":0.47683778405189514},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.3717712163925171},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3019287586212158},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.21270102262496948},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09263190627098083},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce50665.2020.9291972","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce50665.2020.9291972","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 9th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.4399999976158142,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1690739335","https://openalex.org/W1821462560","https://openalex.org/W2312499482","https://openalex.org/W2546005748","https://openalex.org/W2579954811","https://openalex.org/W2773900012","https://openalex.org/W2934775180","https://openalex.org/W2963981420","https://openalex.org/W2964118293","https://openalex.org/W2990578161","https://openalex.org/W3006158531","https://openalex.org/W6724998850"],"related_works":["https://openalex.org/W2098567841","https://openalex.org/W3124209601","https://openalex.org/W2238969010","https://openalex.org/W2189249776","https://openalex.org/W1526105959","https://openalex.org/W3193710367","https://openalex.org/W3122492506","https://openalex.org/W4388046081","https://openalex.org/W1778290058","https://openalex.org/W3124577894"],"abstract_inverted_index":{"Accurate":[0],"flood":[1,13,38,64],"simulations":[2,17],"based":[3,40],"on":[4,41],"hydraulic":[5,42],"models":[6],"have":[7],"been":[8],"performed":[9],"for":[10,31,58],"nowcasting":[11],"pluvial":[12,37],"prediction.":[14],"However,":[15],"real-time":[16],"require":[18],"high-performance":[19],"computers.":[20],"We":[21,44,66],"apply":[22],"knowledge":[23],"distillation":[24,60],"(KD),":[25],"an":[26],"auto":[27],"model":[28],"compression":[29],"approach":[30,80],"large":[32],"deep":[33],"neural":[34,49],"networks,":[35],"to":[36,62,88],"prediction":[39,84,96],"models.":[43],"consider":[45],"a":[46,53,63],"lightweight":[47],"student":[48],"network":[50],"architecture":[51],"and":[52],"loss":[54],"function":[55],"in":[56],"KD":[57,79],"applying":[59],"techniques":[61],"simulation.":[65],"evaluate":[67],"real":[68],"precipitation":[69],"data":[70],"obtained":[71],"using":[72],"rainfall":[73],"radars.":[74],"Results":[75],"show":[76],"that":[77],"the":[78,83,95],"can":[81],"reduce":[82],"time":[85],"by":[86],"up":[87],"0.32%":[89],"(313.34":[90],"\u00d7":[91],"faster)":[92],"while":[93],"maintaining":[94],"accuracy.":[97],"The":[98],"mean":[99],"squared":[100],"error":[101],"is":[102],"1.67":[103],"cm.":[104]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}