{"id":"https://openalex.org/W4391406981","doi":"https://doi.org/10.1109/tgrs.2024.3360636","title":"AdaNAS: Adaptively Postprocessing With Self-Supervised Neural Architecture Search for Ensemble Rainfall Forecasts","display_name":"AdaNAS: Adaptively Postprocessing With Self-Supervised Neural Architecture Search for Ensemble Rainfall Forecasts","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391406981","doi":"https://doi.org/10.1109/tgrs.2024.3360636"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3360636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3360636","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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/A5072037382","display_name":"Yingpeng Wen","orcid":"https://orcid.org/0000-0001-9372-1453"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingpeng Wen","raw_affiliation_strings":["School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0001-9372-1453","affiliations":[{"raw_affiliation_string":"School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055989750","display_name":"Weijiang Yu","orcid":"https://orcid.org/0000-0002-7449-3093"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijiang Yu","raw_affiliation_strings":["School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-7449-3093","affiliations":[{"raw_affiliation_string":"School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024126710","display_name":"Fudan Zheng","orcid":"https://orcid.org/0000-0002-9664-012X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fudan Zheng","raw_affiliation_strings":["School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-9664-012X","affiliations":[{"raw_affiliation_string":"School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041534890","display_name":"Dan Huang","orcid":"https://orcid.org/0000-0001-5582-1031"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Huang","raw_affiliation_strings":["School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0001-5582-1031","affiliations":[{"raw_affiliation_string":"School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023506057","display_name":"Nong Xiao","orcid":"https://orcid.org/0000-0002-2166-977X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nong Xiao","raw_affiliation_strings":["School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Sun Yat-sen University, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.8237,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.66555993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9983999729156494,"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/T11234","display_name":"Precipitation Measurement and Analysis","score":0.9976000189781189,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5929992198944092},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5782695412635803},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5189509391784668},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49863529205322266},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4505331516265869},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.443937748670578},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.31736838817596436},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2749011814594269}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5929992198944092},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5782695412635803},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5189509391784668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49863529205322266},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4505331516265869},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.443937748670578},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31736838817596436},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2749011814594269},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2024.3360636","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3360636","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G2139608131","display_name":null,"funder_award_id":"2018B030312002","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G8452570760","display_name":null,"funder_award_id":"U1811464","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1666703693","https://openalex.org/W2040503026","https://openalex.org/W2060742029","https://openalex.org/W2081736498","https://openalex.org/W2086796296","https://openalex.org/W2112863322","https://openalex.org/W2149763719","https://openalex.org/W2174913527","https://openalex.org/W2194775991","https://openalex.org/W2885487916","https://openalex.org/W2915992092","https://openalex.org/W2951104886","https://openalex.org/W2964259004","https://openalex.org/W2966284335","https://openalex.org/W2994731810","https://openalex.org/W2996409713","https://openalex.org/W3006264628","https://openalex.org/W3016791436","https://openalex.org/W3034528892","https://openalex.org/W3034535818","https://openalex.org/W3034764953","https://openalex.org/W3034906194","https://openalex.org/W3035189477","https://openalex.org/W3035524453","https://openalex.org/W3108051726","https://openalex.org/W3110978130","https://openalex.org/W3127742799","https://openalex.org/W3140878669","https://openalex.org/W3176194858","https://openalex.org/W3203335272","https://openalex.org/W4200309788","https://openalex.org/W4213041519","https://openalex.org/W4245525356","https://openalex.org/W4287900329","https://openalex.org/W6752515464","https://openalex.org/W6756887525","https://openalex.org/W6760018943","https://openalex.org/W6762718338","https://openalex.org/W6771127686","https://openalex.org/W6771784742","https://openalex.org/W6772103215","https://openalex.org/W6786730953","https://openalex.org/W6796538260"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"Previous":[0],"post-processing":[1,74],"studies":[2],"on":[3,13,141],"rainfall":[4,72,77],"forecasts":[5,92],"using":[6],"numerical":[7],"weather":[8],"prediction":[9,204],"(NWP)":[10],"mainly":[11],"focus":[12],"statistics-based":[14],"aspects,":[15],"while":[16],"learning-based":[17],"aspects":[18],"are":[19,26,32,164],"rarely":[20],"investigated.":[21],"Although":[22],"some":[23],"manually-designed":[24],"models":[25],"proposed":[27,66,161,172,196,210],"to":[28,37,70,89,103],"raise":[29],"accuracy,":[30],"they":[31],"customized":[33],"networks,":[34],"which":[35],"need":[36],"be":[38],"repeatedly":[39],"tried":[40],"and":[41,49,75,137,154,166,183,192,205,220,225],"verified,":[42],"at":[43],"a":[44,52,85,99,142],"huge":[45],"cost":[46],"in":[47,67,199],"time":[48],"labor.":[50],"Therefore,":[51],"self-supervised":[53],"neural":[54,179],"architecture":[55,180],"search":[56,87,181],"(NAS)":[57],"method":[58],"without":[59],"significant":[60],"manual":[61,216],"efforts":[62],"called":[63],"AdaNAS":[64,162,173,197,211],"is":[65,175],"this":[68],"study":[69],"perform":[71],"forecast":[73],"predict":[76],"with":[78,177,218],"high":[79],"accuracy.":[80],"In":[81],"addition,":[82],"we":[83,97],"design":[84],"rainfall-aware":[86],"space":[88],"significantly":[90],"improve":[91],"for":[93],"high-rainfall":[94],"areas.":[95],"Furthermore,":[96],"propose":[98],"rainfall-level":[100],"regularization":[101],"function":[102],"eliminate":[104],"the":[105,111,119,149,160,171,189,195,209],"effect":[106],"of":[107,121,159,194,201],"noise":[108],"data":[109],"during":[110],"training.":[112],"Validation":[113],"experiments":[114],"have":[115],"been":[116],"performed":[117],"under":[118],"cases":[120],"<italic":[122,126,130,134,138],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[123,127,131,135,139],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">None</i>":[124],",":[125,129,133],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Light</i>":[128],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Moderate</i>":[132],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Heavy</i>":[136],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Violent</i>":[140],"large-scale":[143],"precipitation":[144,202],"benchmark":[145],"named":[146],"TIGGE.":[147],"Finally,":[148],"average":[150,155],"mean-absolute":[151],"error":[152,157],"(MAE)":[153],"root-mean-square":[156],"(RMSE)":[158],"model":[163,174,198,212],"0.98":[165],"2.04":[167],"mm/day,":[168],"respectively.":[169,227],"Additionally,":[170],"compared":[176],"other":[178],"methods":[182,217],"previous":[184,214],"studies.":[185],"Compared":[186],"results":[187],"reveal":[188],"satisfactory":[190],"performance":[191],"superiority":[193],"terms":[200],"amount":[203],"intensity":[206],"classification.":[207],"Concretely,":[208],"outperformed":[213],"best-performing":[215],"MAE":[219],"RMSE":[221],"improving":[222],"by":[223],"80.5%":[224],"80.3%,":[226]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
