{"id":"https://openalex.org/W4225540241","doi":"https://doi.org/10.1145/3498333","title":"RSDF-AM-LSTM: Regional Scale Division Rainfall Forecasting Using Attention and LSTM","display_name":"RSDF-AM-LSTM: Regional Scale Division Rainfall Forecasting Using Attention and LSTM","publication_year":2021,"publication_date":"2021-11-30","ids":{"openalex":"https://openalex.org/W4225540241","doi":"https://doi.org/10.1145/3498333"},"language":"en","primary_location":{"id":"doi:10.1145/3498333","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3498333","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3498333","source":{"id":"https://openalex.org/S4210185969","display_name":"ACM/IMS Transactions on Data Science","issn_l":"2577-3224","issn":["2577-3224","2691-1922"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM/IMS Transactions on Data Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3498333","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100739624","display_name":"Wenrui Li","orcid":"https://orcid.org/0000-0002-9828-4691"},"institutions":[{"id":"https://openalex.org/I4210128418","display_name":"Nanjing Xiaozhuang University","ror":"https://ror.org/03fnv7n42","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210128418"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenrui Li","raw_affiliation_strings":["Nanjing Xiao Zhuang University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing Xiao Zhuang University, China","institution_ids":["https://openalex.org/I4210128418"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035407903","display_name":"Pengcheng Zhang","orcid":"https://orcid.org/0000-0003-3594-408X"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengcheng Zhang","raw_affiliation_strings":["Hohai University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005267734","display_name":"Hai Dong","orcid":"https://orcid.org/0000-0002-7033-5688"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hai Dong","raw_affiliation_strings":["RMIT University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026694879","display_name":"Yangyang Jia","orcid":"https://orcid.org/0000-0002-9324-5052"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangyang Jia","raw_affiliation_strings":["Hohai University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065521868","display_name":"Wennan Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wennan Cao","raw_affiliation_strings":["Hohai University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University, China","institution_ids":["https://openalex.org/I163340411"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1168,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49645252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"2","issue":"4","first_page":"1","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9987999796867371,"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":0.9987999796867371,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9976999759674072,"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":0.9954000115394592,"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":0.7341358661651611},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6400087475776672},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6346744298934937},{"id":"https://openalex.org/keywords/division","display_name":"Division (mathematics)","score":0.6286089420318604},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.602867603302002},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.5257316827774048},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5164316296577454},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4527949094772339},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.418023943901062},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37495630979537964},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37144118547439575},{"id":"https://openalex.org/keywords/climatology","display_name":"Climatology","score":0.33940500020980835},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.28949153423309326},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09691539406776428},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09685084223747253},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09416341781616211},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08149448037147522}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7341358661651611},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6400087475776672},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6346744298934937},{"id":"https://openalex.org/C60798267","wikidata":"https://www.wikidata.org/wiki/Q1226939","display_name":"Division (mathematics)","level":2,"score":0.6286089420318604},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.602867603302002},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.5257316827774048},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5164316296577454},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4527949094772339},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.418023943901062},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37495630979537964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37144118547439575},{"id":"https://openalex.org/C49204034","wikidata":"https://www.wikidata.org/wiki/Q52139","display_name":"Climatology","level":1,"score":0.33940500020980835},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.28949153423309326},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09691539406776428},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09685084223747253},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09416341781616211},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08149448037147522},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3498333","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3498333","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3498333","source":{"id":"https://openalex.org/S4210185969","display_name":"ACM/IMS Transactions on Data Science","issn_l":"2577-3224","issn":["2577-3224","2691-1922"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM/IMS Transactions on Data Science","raw_type":"journal-article"},{"id":"pmh:oai:figshare.com:article/27569754","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/RSDF-AM-LSTM_Regional_Scale_Division_Rainfall_Forecasting_Using_Attention_and_LSTM/27569754","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1145/3498333","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3498333","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3498333","source":{"id":"https://openalex.org/S4210185969","display_name":"ACM/IMS Transactions on Data Science","issn_l":"2577-3224","issn":["2577-3224","2691-1922"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM/IMS Transactions on Data Science","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G1149272097","display_name":null,"funder_award_id":"B210202075","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G2483428696","display_name":null,"funder_award_id":"BK20191297","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321605","display_name":"Government of Jiangsu Province","ror":"https://ror.org/004svx814"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325416","display_name":"Nanjing Xiaozhuang University","ror":"https://ror.org/03fnv7n42"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4225540241.pdf","grobid_xml":"https://content.openalex.org/works/W4225540241.grobid-xml"},"referenced_works_count":46,"referenced_works":["https://openalex.org/W586775178","https://openalex.org/W1422238814","https://openalex.org/W1591760165","https://openalex.org/W1964425467","https://openalex.org/W1980160042","https://openalex.org/W1994319526","https://openalex.org/W1995427974","https://openalex.org/W2011787805","https://openalex.org/W2033310064","https://openalex.org/W2035930921","https://openalex.org/W2044893557","https://openalex.org/W2048897976","https://openalex.org/W2067562186","https://openalex.org/W2076063813","https://openalex.org/W2088492460","https://openalex.org/W2092556113","https://openalex.org/W2109110888","https://openalex.org/W2136848157","https://openalex.org/W2147800946","https://openalex.org/W2170334405","https://openalex.org/W2179093417","https://openalex.org/W2257979135","https://openalex.org/W2267272611","https://openalex.org/W2295038166","https://openalex.org/W2305335410","https://openalex.org/W2326699005","https://openalex.org/W2338068721","https://openalex.org/W2375828769","https://openalex.org/W2554853555","https://openalex.org/W2559655401","https://openalex.org/W2604271147","https://openalex.org/W2612690371","https://openalex.org/W2773055204","https://openalex.org/W2791488060","https://openalex.org/W2810006455","https://openalex.org/W2887712318","https://openalex.org/W2890034837","https://openalex.org/W2898796705","https://openalex.org/W2919115771","https://openalex.org/W3004243356","https://openalex.org/W3034709323","https://openalex.org/W3086729350","https://openalex.org/W3208425414","https://openalex.org/W4254300870","https://openalex.org/W4300703388","https://openalex.org/W7046023151"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W4375867731","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2080152487","https://openalex.org/W4213091376","https://openalex.org/W4252317921","https://openalex.org/W3168412960"],"abstract_inverted_index":{"Short-term":[0],"rainfall":[1,22,51,56,99,114,142,165,180],"forecasting":[2,57,102],"plays":[3],"a":[4,38,50,78,121,131],"critical":[5],"role":[6],"in":[7,21,60,69,183,192],"meteorology,":[8],"hydrology,":[9],"and":[10,40,88,97,133,149,170,193,207],"other":[11,208],"related":[12],"areas.":[13],"Currently,":[14],"data-driven":[15],"approaches":[16,26],"have":[17],"made":[18],"considerable":[19],"progress":[20],"forecasting.":[23,100],"However,":[24],"these":[25,70,74],"suffer":[27],"from":[28,188],"the":[29,46,109,113,138,162],"following":[30],"major":[31],"drawbacks.":[32],"First,":[33],"they":[34],"do":[35],"not":[36,66],"accommodate":[37],"reasonable":[39,132],"effective":[41,134],"model":[42,84,103,135],"to":[43,124,136,160],"mathematically":[44],"represent":[45],"complete":[47,139],"changes":[48,140],"of":[49,108,112,141,164],"block.":[52,166],"Second,":[53],"scale":[54],"division":[55],"is":[58,65,117,158],"overlooked":[59],"existing":[61],"literature.":[62],"Third,":[63],"generalization":[64],"well":[67],"validated":[68],"approaches.":[71,212],"To":[72],"address":[73],"issues,":[75],"we":[76],"propose":[77],"novel":[79],"Regional":[80],"Scale":[81],"Division":[82],"Forecasting":[83],"using":[85],"attention":[86],"mechanism":[87],"long":[89],"short":[90],"term":[91],"memory":[92],"network":[93],"(RSDF-AM-LSTM)":[94],"for":[95],"short-term":[96],"scale-division":[98],"The":[101,127,179,196],"can":[104],"take":[105],"full":[106],"account":[107],"regional":[110],"characteristics":[111],"blocks.":[115],"It":[116],"established":[118],"by":[119],"employing":[120],"method":[122],"similar":[123],"image":[125],"evolution.":[126],"approach":[128],"also":[129],"provides":[130],"formulate":[137],"blocks,":[143],"including":[144],"newborn,":[145],"splitting,":[146],"strengthening,":[147],"weakening,":[148],"merging.":[150],"A":[151],"deep":[152],"learning":[153,211],"algorithm":[154],"based":[155],"on":[156],"AM-LSTM":[157,167],"proposed":[159],"forecast":[161],"change":[163],"describes":[168],"temporal":[169],"spatial":[171],"association":[172],"among":[173],"four":[174],"parallel":[175],"LSTMs":[176],"through":[177],"AM.":[178],"data":[181],"used":[182],"our":[184],"experiments":[185],"are":[186],"obtained":[187],"3,200":[189],"meteorological":[190],"stations":[191],"around":[194],"China.":[195],"experimental":[197],"results":[198],"show":[199],"that":[200],"RSDF-AM-LSTM":[201],"outperforms":[202],"two":[203],"popular":[204],"atmospheric":[205],"models":[206],"traditional":[209],"machine":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}