{"id":"https://openalex.org/W4213077516","doi":"https://doi.org/10.1145/3488560.3498448","title":"A New Class of Polynomial Activation Functions of Deep Learning for Precipitation Forecasting","display_name":"A New Class of Polynomial Activation Functions of Deep Learning for Precipitation Forecasting","publication_year":2022,"publication_date":"2022-02-11","ids":{"openalex":"https://openalex.org/W4213077516","doi":"https://doi.org/10.1145/3488560.3498448"},"language":"en","primary_location":{"id":"doi:10.1145/3488560.3498448","doi":"https://doi.org/10.1145/3488560.3498448","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488560.3498448","pdf_url":null,"source":{"id":"https://openalex.org/S4363608885","display_name":"ACM International Conference on Web Search and Data Mining (WSDM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5013205685","display_name":"Jiachuan Wang","orcid":"https://orcid.org/0000-0001-6473-8221","observed_orcids":["https://orcid.org/0000-0001-6473-8221"]},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jiachuan Wang","raw_affiliation_strings":["Hong Kong University of Science and Technology, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Hong Kong, China","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100333516","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0002-8257-5806","observed_orcids":["https://orcid.org/0000-0002-8257-5806","https://orcid.org/0000-0002-1054-5501","https://orcid.org/0000-0002-4912-3293","https://orcid.org/0000-0002-7918-8936","https://orcid.org/0000-0003-4664-0700"]},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["Hong Kong University of Science and Technology, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Hong Kong, China","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047314253","display_name":"Charles Wang Wai Ng","orcid":"https://orcid.org/0000-0001-6693-3151","observed_orcids":["https://orcid.org/0000-0001-6693-3151","https://orcid.org/0000-0002-9010-051X"]},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Charles Wang Wai Ng","raw_affiliation_strings":["Hong Kong University of Science and Technology, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Hong Kong, China","institution_ids":["https://openalex.org/I200769079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I200769079"],"apc_list":null,"apc_paid":null,"fwci":5.9249,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.96610509,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1025","last_page":"1035"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.5273000001907349,"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":0.5273000001907349,"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/T10320","display_name":"Neural Networks and Applications","score":0.46480000019073486,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.0026000000070780516,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/polynomial-activation-functions","display_name":"polynomial activation functions","score":0.9950000047683716},{"id":"https://openalex.org/keywords/rainfall-prediction","display_name":"precipitation forecasting","score":0.7889999747276306},{"id":"https://openalex.org/keywords/chebyshev-polynomials","display_name":"Chebyshev polynomials","score":0.9850000143051147},{"id":"https://openalex.org/keywords/training-stability","display_name":"training stability","score":0.7089999914169312},{"id":"https://openalex.org/keywords/deep-neural-network","display_name":"deep neural network","score":0.5680000185966492},{"id":"https://openalex.org/keywords/physics-informed-neural-networks","display_name":"physics-informed neural networks","score":0.6169999837875366},{"id":"https://openalex.org/keywords/chaotic-systems","display_name":"chaotic systems","score":0.5239999890327454},{"id":"https://openalex.org/keywords/summer-rainfall","display_name":"summer precipitation","score":0.4880000054836273}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6484402418136597},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6399621963500977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6238243579864502},{"id":"https://openalex.org/C129785596","wikidata":"https://www.wikidata.org/wiki/Q619511","display_name":"Chebyshev polynomials","level":2,"score":0.6027929782867432},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.48741844296455383},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.4778725206851959},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.47146254777908325},{"id":"https://openalex.org/C145242015","wikidata":"https://www.wikidata.org/wiki/Q774123","display_name":"Approximation theory","level":2,"score":0.4251460134983063},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.4217422902584076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4082157015800476},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39455994963645935},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3258761763572693},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22064897418022156},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3488560.3498448","doi":"https://doi.org/10.1145/3488560.3498448","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3488560.3498448","pdf_url":null,"source":{"id":"https://openalex.org/S4363608885","display_name":"ACM International Conference on Web Search and Data Mining (WSDM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-116138","doi":null,"is_oa":false,"landing_page_url":"http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000810504300109","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference paper"},{"id":"pmh:oai:repository.ust.hk:1783.1-116138","doi":null,"is_oa":false,"landing_page_url":"http://repository.ust.hk/ir/Record/1783.1-116138","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W1973513227","https://openalex.org/W1988115241","https://openalex.org/W2018921004","https://openalex.org/W2046361090","https://openalex.org/W2051968241","https://openalex.org/W2064675550","https://openalex.org/W2073767379","https://openalex.org/W2098787760","https://openalex.org/W2099759795","https://openalex.org/W2103496339","https://openalex.org/W2137983211","https://openalex.org/W2152619728","https://openalex.org/W2158581396","https://openalex.org/W2512971201","https://openalex.org/W2516419770","https://openalex.org/W2521003103","https://openalex.org/W2601622117","https://openalex.org/W2723293840","https://openalex.org/W2770705252","https://openalex.org/W2800702901","https://openalex.org/W2804943168","https://openalex.org/W2909554346","https://openalex.org/W2950179405","https://openalex.org/W4249857356","https://openalex.org/W4301001114"],"related_works":["https://openalex.org/W4416373131","https://openalex.org/W3207482727","https://openalex.org/W3090961927","https://openalex.org/W4414847046","https://openalex.org/W7214966409","https://openalex.org/W7169745897","https://openalex.org/W4399510920","https://openalex.org/W1559836788","https://openalex.org/W4384302471","https://openalex.org/W7144467114"],"abstract_inverted_index":{"Precipitation":[0],"forecasting,":[1],"modeled":[2],"as":[3],"an":[4,40],"important":[5],"chaotic":[6],"system":[7,10,102],"in":[8,29,39,114],"earth":[9],"science,":[11],"is":[12,84,136,146,167,207],"not":[13],"explicitly":[14],"solved":[15],"with":[16,76,125,182,194],"theory-driven":[17,95],"models.":[18],"In":[19,80,122,149],"recent":[20,123],"years,":[21,124],"deep":[22,60,130,193,238],"learning":[23,61,131,239],"models":[24,37],"have":[25],"achieved":[26],"great":[27],"success":[28],"various":[30],"applications":[31],"including":[32],"rainfall":[33],"prediction.":[34],"However,":[35],"these":[36],"work":[38],"image":[41],"processing":[42],"manner":[43],"regardless":[44],"of":[45,48,86,92,143,200,222,226,244],"the":[46,55,67,82,87,109,120,198,220],"nature":[47],"a":[49,140,202,224,241],"physical":[50,232],"system.":[51],"We":[52,173],"found":[53],"that":[54,128,138],"non-linearity":[56],"relationships":[57],"learned":[58],"by":[59],"models,":[62,96],"which":[63,188],"mostly":[64],"rely":[65],"on":[66,211],"activation":[68,111,227],"functions,":[69],"are":[70,161],"commonly":[71],"weighted":[72],"piecewise":[73],"continuous":[74],"functions":[75,93,112,145,228],"bounded":[77],"first-order":[78],"derivatives.":[79],"contrast,":[81],"polynomial":[83,110],"one":[85],"most":[88],"widely":[89],"used":[90],"classes":[91],"for":[94,116,237],"applied":[97],"to":[98,107,132,170,191,229],"numerical":[99],"approximation,":[100],"dynamic":[101],"modeling,":[103],"etc..":[104],"Researchers":[105],"started":[106],"use":[108],"(Pacs":[113],"short)":[115],"neural":[117],"networks":[118,190],"from":[119],"1990s.":[121],"bloomed":[126],"researches":[127],"apply":[129],"scientific":[133],"problems,":[134],"it":[135,154,166],"weird":[137],"such":[139,223],"powerful":[141],"class":[142,225],"basis":[144],"rarely":[147],"used.":[148],"this":[150],"paper,":[151],"we":[152],"investigate":[153],"and":[155,185,214],"argue":[156],"that,":[157],"even":[158],"though":[159],"polynomials":[160,184],"good":[162],"at":[163],"information":[164],"extraction,":[165],"too":[168],"fragile":[169],"train":[171],"stably.":[172],"finally":[174],"solve":[175],"its":[176],"serious":[177],"data":[178],"flow":[179],"explosion":[180],"problem":[181],"Chebyshev":[183],"prepended":[186],"normalization,":[187],"enables":[189],"go":[192],"Pacs.":[195],"To":[196],"enhance":[197],"robustness":[199],"training,":[201],"normalization":[203],"called":[204],"Range":[205],"Norm":[206],"further":[208],"proposed.":[209],"Performance":[210],"synthetic":[212],"dataset":[213],"summer":[215],"precipitation":[216],"prediction":[217],"task":[218],"validates":[219],"necessity":[221],"simulate":[230],"complex":[231],"mechanisms.":[233],"The":[234],"new":[235,242],"tool":[236],"enlightens":[240],"way":[243],"automatic":[245],"theoretical":[246],"physics":[247],"analysis.":[248]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2022-02-24T00:00:00"}
