{"id":"https://openalex.org/W4313130561","doi":"https://doi.org/10.1109/icpr56361.2022.9956046","title":"MMINR: Multi-frame-to-Multi-frame Inference with Noise Resistance for Precipitation Nowcasting with Radar","display_name":"MMINR: Multi-frame-to-Multi-frame Inference with Noise Resistance for Precipitation Nowcasting with Radar","publication_year":2022,"publication_date":"2022-08-21","ids":{"openalex":"https://openalex.org/W4313130561","doi":"https://doi.org/10.1109/icpr56361.2022.9956046"},"language":"en","primary_location":{"id":"doi:10.1109/icpr56361.2022.9956046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956046","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"2022 26th International Conference on Pattern Recognition (ICPR)","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/A5006692615","display_name":"Feng Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Sun","raw_affiliation_strings":["Zhejiang University of Technology,College of Computer Science and Technology,Hangzhou,China,310023"],"affiliations":[{"raw_affiliation_string":"Zhejiang University of Technology,College of Computer Science and Technology,Hangzhou,China,310023","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013768600","display_name":"Cong Bai","orcid":"https://orcid.org/0000-0002-6177-3862"},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cong Bai","raw_affiliation_strings":["Zhejiang University of Technology,College of Computer Science and Technology,Hangzhou,China,310023"],"affiliations":[{"raw_affiliation_string":"Zhejiang University of Technology,College of Computer Science and Technology,Hangzhou,China,310023","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009701259","display_name":"Yi Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Song","raw_affiliation_strings":["Technology Company Limited,Piesat Information,Beijing,China,100195"],"affiliations":[{"raw_affiliation_string":"Technology Company Limited,Piesat Information,Beijing,China,100195","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100687480","display_name":"Jinglin Zhang","orcid":"https://orcid.org/0000-0003-1618-8493"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinglin Zhang","raw_affiliation_strings":["Shandong University,School of Control Science and Engineering,Jinan,China,250061"],"affiliations":[{"raw_affiliation_string":"Shandong University,School of Control Science and Engineering,Jinan,China,250061","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5006692615"],"corresponding_institution_ids":["https://openalex.org/I55712492"],"apc_list":null,"apc_paid":null,"fwci":2.1686,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.88972973,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"97","last_page":"103"},"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/T11234","display_name":"Precipitation Measurement and Analysis","score":0.9994000196456909,"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/T10930","display_name":"Flood Risk Assessment and Management","score":0.9983000159263611,"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/computer-science","display_name":"Computer science","score":0.788622260093689},{"id":"https://openalex.org/keywords/nowcasting","display_name":"Nowcasting","score":0.7175543308258057},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.650416910648346},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6311754584312439},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6083225607872009},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5322990417480469},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.525233268737793},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45825281739234924},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4478512704372406},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37582147121429443},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3557034134864807},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3438960313796997},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12406015396118164},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.09156626462936401}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.788622260093689},{"id":"https://openalex.org/C2781013037","wikidata":"https://www.wikidata.org/wiki/Q1433331","display_name":"Nowcasting","level":2,"score":0.7175543308258057},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.650416910648346},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6311754584312439},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6083225607872009},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5322990417480469},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.525233268737793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45825281739234924},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4478512704372406},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37582147121429443},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3557034134864807},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3438960313796997},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12406015396118164},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.09156626462936401},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr56361.2022.9956046","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr56361.2022.9956046","pdf_url":null,"source":{"id":"https://openalex.org/S4363607731","display_name":"2022 26th International Conference on Pattern Recognition (ICPR)","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":"2022 26th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.8100000023841858}],"awards":[],"funders":[{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2024414272","https://openalex.org/W2171314103","https://openalex.org/W2625614184","https://openalex.org/W2768975186","https://openalex.org/W2784025318","https://openalex.org/W2796873224","https://openalex.org/W2884585870","https://openalex.org/W2967033144","https://openalex.org/W3009216045","https://openalex.org/W3109184532","https://openalex.org/W3126335003","https://openalex.org/W4210904397","https://openalex.org/W4293192773","https://openalex.org/W6628877408","https://openalex.org/W6639824700","https://openalex.org/W6739112683","https://openalex.org/W6745829810","https://openalex.org/W6750259706","https://openalex.org/W6753412334"],"related_works":["https://openalex.org/W2098567841","https://openalex.org/W3124209601","https://openalex.org/W2238969010","https://openalex.org/W2189249776","https://openalex.org/W2945625440","https://openalex.org/W1526105959","https://openalex.org/W3193710367","https://openalex.org/W3122492506","https://openalex.org/W3004779417","https://openalex.org/W1778290058"],"abstract_inverted_index":{"Precipitation":[0],"nowcasting":[1,61],"based":[2,15,34],"on":[3],"radar":[4],"echo":[5],"maps":[6],"is":[7,80],"essential":[8],"in":[9,29,63,121],"meteorological":[10],"research.":[11],"Recently,":[12],"Convolutional":[13],"RNNs":[14],"methods":[16,35],"dominate":[17],"this":[18,44,95],"field,":[19],"but":[20],"they":[21],"cannot":[22],"be":[23],"solved":[24],"by":[25,160],"parallel":[26,122],"computation":[27],"resulting":[28],"longer":[30],"inference":[31,39],"time.":[32],"FCN":[33],"adopt":[36],"a":[37,81,99,126,132],"multi-frame-to-single-frame":[38],"(MSI)":[40],"strategy":[41],"to":[42,52,58,70,85,151],"avoid":[43],"problem.":[45],"They":[46],"feedback":[47],"into":[48],"the":[49,54,64,71,153,180,187],"model":[50,104],"again":[51],"predict":[53],"next":[55],"time":[56],"step":[57],"get":[59],"multi-frame":[60],"results":[62,163],"prediction":[65,74,87],"phase,":[66],"which":[67],"will":[68],"lead":[69],"accumulation":[72,114],"of":[73,90,149,155],"errors.":[75],"In":[76],"addition,":[77],"precipitation":[78,117],"noise":[79,140],"crucial":[82],"factor":[83],"contributing":[84],"high":[86],"errors":[88],"because":[89],"its":[91],"unpredictability.":[92],"To":[93],"address":[94],"problem,":[96],"we":[97],"propose":[98],"novel":[100],"Multi-frame-to-Multi-frame":[101],"Inference":[102],"(MMI)":[103],"with":[105,172],"Noise":[106,127],"Resistance":[107],"(NR)":[108],"named":[109],"MMINR.":[110],"It":[111],"avoids":[112],"error":[113],"and":[115,131,144,183],"resists":[116],"noise\u015b":[118],"negative":[119],"effect":[120],"computation.":[123],"NR":[124],"contains":[125],"Dropout":[128],"Module":[129,135],"(NDM)":[130],"Semantic":[133],"Restore":[134],"(SRM).":[136],"NDM":[137,182],"deliberately":[138],"dropout":[139],"simple":[141],"yet":[142],"efficient,":[143],"SRM":[145,184],"supplements":[146],"semantic":[147,156],"information":[148,157],"features":[150],"alleviate":[152],"problem":[154],"mistakenly":[158],"lost":[159],"NDM.":[161],"Experimental":[162],"demonstrate":[164],"that":[165,179],"MMINR":[166],"can":[167,185],"attain":[168],"competitive":[169],"scores":[170],"compared":[171],"other":[173],"SOTAs.":[174],"The":[175],"ablation":[176],"experiments":[177],"show":[178],"proposed":[181],"solve":[186],"aforementioned":[188],"problems.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}