{"id":"https://openalex.org/W2951554591","doi":"https://doi.org/10.1145/3292500.3330717","title":"LightNet","display_name":"LightNet","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2951554591","doi":"https://doi.org/10.1145/3292500.3330717","mag":"2951554591"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330717","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330717","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","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/A5018191388","display_name":"Yangli\u2010ao Geng","orcid":"https://orcid.org/0000-0003-0173-4041"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yangli-ao Geng","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005350496","display_name":"Qingyong Li","orcid":"https://orcid.org/0000-0002-3860-4809"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyong Li","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063042566","display_name":"Tianyang Lin","orcid":"https://orcid.org/0000-0002-1228-8385"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyang Lin","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101867733","display_name":"Lei Jiang","orcid":"https://orcid.org/0000-0002-7478-646X"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Jiang","raw_affiliation_strings":["Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080303452","display_name":"Liangtao Xu","orcid":"https://orcid.org/0000-0001-7901-2925"},"institutions":[{"id":"https://openalex.org/I4210133131","display_name":"Chinese Academy of Meteorological Sciences","ror":"https://ror.org/034b53w38","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210133131"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangtao Xu","raw_affiliation_strings":["Chinese Academy of Meteorological Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Meteorological Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210133131"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062446951","display_name":"Dong Zheng","orcid":"https://orcid.org/0000-0002-1880-3296"},"institutions":[{"id":"https://openalex.org/I4210133131","display_name":"Chinese Academy of Meteorological Sciences","ror":"https://ror.org/034b53w38","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210133131"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Zheng","raw_affiliation_strings":["Chinese Academy of Meteorological Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Meteorological Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210133131"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058751400","display_name":"Wen Yao","orcid":"https://orcid.org/0000-0002-0622-8095"},"institutions":[{"id":"https://openalex.org/I4210133131","display_name":"Chinese Academy of Meteorological Sciences","ror":"https://ror.org/034b53w38","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210133131"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Yao","raw_affiliation_strings":["Chinese Academy of Meteorological Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Meteorological Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210133131"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008813351","display_name":"Weitao Lyu","orcid":"https://orcid.org/0000-0001-8434-6655"},"institutions":[{"id":"https://openalex.org/I4210133131","display_name":"Chinese Academy of Meteorological Sciences","ror":"https://ror.org/034b53w38","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210133131"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weitao Lyu","raw_affiliation_strings":["Chinese Academy of Meteorological Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Meteorological Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210133131"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100376298","display_name":"Yijun Zhang","orcid":"https://orcid.org/0000-0002-7573-424X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yijun Zhang","raw_affiliation_strings":["Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5018191388"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":2.5544,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.89264476,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2439","last_page":"2447"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.9926999807357788,"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/T10555","display_name":"Fire effects on ecosystems","score":0.9926999807357788,"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/T10535","display_name":"Landslides and related hazards","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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":0.989300012588501,"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/lightning","display_name":"Lightning (connector)","score":0.7538586258888245},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6744616031646729},{"id":"https://openalex.org/keywords/numerical-weather-prediction","display_name":"Numerical weather prediction","score":0.6266058087348938},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.4469109773635864},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4312573969364166},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.40126341581344604},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3858606815338135},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2657889127731323},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10602721571922302}],"concepts":[{"id":"https://openalex.org/C69398868","wikidata":"https://www.wikidata.org/wiki/Q129052","display_name":"Lightning (connector)","level":3,"score":0.7538586258888245},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6744616031646729},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.6266058087348938},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.4469109773635864},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4312573969364166},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.40126341581344604},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3858606815338135},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2657889127731323},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10602721571922302},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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:10.1145/3292500.3330717","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330717","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.5600000023841858,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W589686938","https://openalex.org/W1485009520","https://openalex.org/W1522734439","https://openalex.org/W1745334888","https://openalex.org/W1963731102","https://openalex.org/W1979959032","https://openalex.org/W1982479097","https://openalex.org/W1987324508","https://openalex.org/W2003461740","https://openalex.org/W2047838924","https://openalex.org/W2051027109","https://openalex.org/W2062082461","https://openalex.org/W2063625323","https://openalex.org/W2070381285","https://openalex.org/W2112546795","https://openalex.org/W2152984210","https://openalex.org/W2155027475","https://openalex.org/W2345707736","https://openalex.org/W2754757181","https://openalex.org/W2764257027","https://openalex.org/W2768348081","https://openalex.org/W2768975186","https://openalex.org/W2782928796","https://openalex.org/W2792141117","https://openalex.org/W2808011586","https://openalex.org/W2809444759","https://openalex.org/W2963358464","https://openalex.org/W2963866658","https://openalex.org/W2964121744"],"related_works":["https://openalex.org/W3081577063","https://openalex.org/W3110237217","https://openalex.org/W4405031167","https://openalex.org/W2184710923","https://openalex.org/W3133000293","https://openalex.org/W3155716677","https://openalex.org/W2526518095","https://openalex.org/W2321020009","https://openalex.org/W4383218913","https://openalex.org/W4315472244"],"abstract_inverted_index":{"Lightning":[0,15],"as":[1,107],"a":[2,18,98,160,170,181,192],"natural":[3],"phenomenon":[4],"poses":[5],"serious":[6],"threats":[7],"to":[8,66,106,133,173],"human":[9],"life,":[10],"aviation":[11],"and":[12,59,137,149],"electrical":[13],"infrastructures.":[14],"prediction":[16,26,115,201],"plays":[17],"vital":[19],"role":[20],"in":[21,57,80,130,195],"lightning":[22,36,72,110,128,185],"disaster":[23],"reduction.":[24],"Existing":[25],"methods,":[27,42],"usually":[28,54],"based":[29,101,120],"on":[30,35,102,121,180],"numerical":[31,51,122],"weather":[32,52,123],"models,":[33,124],"rely":[34],"parameterization":[37,68,73],"schemes":[38,74,85],"for":[39,109,199],"forecasting.":[40],"These":[41,154],"however,":[43],"have":[44,55],"two":[45],"drawbacks.":[46],"Firstly,":[47],"simulations":[48,136,148],"of":[49,146],"the":[50,71,113,135,139,147,164],"models":[53],"deviations":[56],"space":[58],"time":[60],"domains,":[61],"which":[62,82,117,187],"introduces":[63,126],"irreparable":[64],"biases":[65],"subsequent":[67],"processes.":[69],"Secondly,":[70],"are":[75,118,156,167],"designed":[76],"manually":[77],"by":[78,159],"experts":[79],"meteorology,":[81],"means":[83],"these":[84],"can":[86],"hardly":[87],"benefit":[88],"from":[89],"abundant":[90],"historical":[91],"data.":[92],"In":[93],"this":[94],"work,":[95],"we":[96],"propose":[97],"data-driven":[99],"model":[100],"neural":[103],"networks,":[104],"referred":[105],"LightNet,":[108],"prediction.":[111,140],"Unlike":[112],"conventional":[114],"methods":[116],"fully":[119],"LightNet":[125,141,190],"recent":[127],"observations":[129,150],"an":[131],"attempt":[132],"calibrate":[134],"assist":[138],"first":[142],"extracts":[143],"spatiotemporal":[144,171],"features":[145,155,166],"via":[151],"dual":[152],"encoders.":[153],"then":[157],"combined":[158],"fusion":[161],"module.":[162],"Finally,":[163],"fused":[165],"fed":[168],"into":[169],"decoder":[172],"make":[174],"forecasts.":[175],"We":[176],"conduct":[177],"experimental":[178],"evaluations":[179],"real-world":[182],"North":[183],"China":[184],"dataset,":[186],"shows":[188],"that":[189],"achieves":[191],"threefold":[193],"improvement":[194],"equitable":[196],"threat":[197],"score":[198],"six-hour":[200],"compared":[202],"with":[203],"three":[204],"established":[205],"forecast":[206],"methods.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2019-06-27T00:00:00"}