{"id":"https://openalex.org/W2977480440","doi":"https://doi.org/10.1109/ijcnn.2019.8851968","title":"NAO Index Prediction using LSTM and ConvLSTM Networks Coupled with Discrete Wavelet Transform","display_name":"NAO Index Prediction using LSTM and ConvLSTM Networks Coupled with Discrete Wavelet Transform","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2977480440","doi":"https://doi.org/10.1109/ijcnn.2019.8851968","mag":"2977480440"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8851968","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851968","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5081341561","display_name":"Bin Mu","orcid":"https://orcid.org/0000-0003-4414-9811"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bin Mu","raw_affiliation_strings":["Department of Software Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Software Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100683041","display_name":"Jing Li","orcid":"https://orcid.org/0000-0001-9933-8383"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":["Department of Software Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Software Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006993955","display_name":"Shijin Yuan","orcid":"https://orcid.org/0000-0002-8102-3137"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shijin Yuan","raw_affiliation_strings":["Department of Software Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Software Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033525844","display_name":"Xiaodan Luo","orcid":"https://orcid.org/0000-0003-4272-5782"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodan Luo","raw_affiliation_strings":["Department of Software Engineering, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Software Engineering, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059327652","display_name":"Guokun Dai","orcid":"https://orcid.org/0000-0001-5303-2952"},"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":"Guokun Dai","raw_affiliation_strings":["Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5081341561"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":0.5109,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67249174,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"36","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.9972000122070312,"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/T10029","display_name":"Climate variability and models","score":0.9972000122070312,"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9965999722480774,"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":0.9894000291824341,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6701722741127014},{"id":"https://openalex.org/keywords/discrete-wavelet-transform","display_name":"Discrete wavelet transform","score":0.6393499970436096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5994879007339478},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5098082423210144},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47552254796028137},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4596829414367676},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.45276346802711487},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4505392909049988},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4253655672073364},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35714614391326904},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.3191015124320984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6701722741127014},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.6393499970436096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5994879007339478},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5098082423210144},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47552254796028137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4596829414367676},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.45276346802711487},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4505392909049988},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4253655672073364},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35714614391326904},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.3191015124320984},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8851968","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8851968","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1506729524","https://openalex.org/W1985854228","https://openalex.org/W2014809997","https://openalex.org/W2022390317","https://openalex.org/W2076274466","https://openalex.org/W2093454912","https://openalex.org/W2108073166","https://openalex.org/W2119844460","https://openalex.org/W2156205656","https://openalex.org/W2166429424","https://openalex.org/W2172680857","https://openalex.org/W2519140045","https://openalex.org/W2536941549","https://openalex.org/W2550913332","https://openalex.org/W2560846835","https://openalex.org/W2604882439","https://openalex.org/W2614262481","https://openalex.org/W2762689859","https://openalex.org/W2767264170","https://openalex.org/W2790485899","https://openalex.org/W2799572365","https://openalex.org/W2804687177","https://openalex.org/W2884415573","https://openalex.org/W2887743720","https://openalex.org/W2888408766","https://openalex.org/W2899816880","https://openalex.org/W2963547740","https://openalex.org/W4248911612","https://openalex.org/W4255657564","https://openalex.org/W6745071918","https://openalex.org/W6745526139"],"related_works":["https://openalex.org/W183670115","https://openalex.org/W1501179639","https://openalex.org/W3199035354","https://openalex.org/W2085792030","https://openalex.org/W1807354010","https://openalex.org/W3143644526","https://openalex.org/W598225674","https://openalex.org/W2734230146","https://openalex.org/W1588899229","https://openalex.org/W2077021924"],"abstract_inverted_index":{"The":[0,31,114,217],"North":[1],"Atlantic":[2],"Oscillation":[3],"(NAO)":[4],"has":[5,18,49],"a":[6],"significant":[7],"effect":[8],"on":[9,125,249],"the":[10,28,36,50,57,64,73,77,94,109,126,134,158,171,175,178,186,195],"global":[11],"weather":[12],"and":[13,48,54,111,152,162,177,197,224],"climate":[14],"variation.":[15],"Thus,":[16],"it":[17],"widespread":[19],"scientific":[20],"research":[21],"value":[22],"to":[23,75,92,102,132,147,239],"enhance":[24],"prediction":[25,135],"skill":[26],"of":[27,52,59,66],"NAO":[29,32,37,78,95,116,127,159],"events.":[30],"is":[33,90,100,192],"quantified":[34],"by":[35,121],"index":[38,79,96,117,160],"which":[39],"can":[40,118],"be":[41,119],"defined":[42],"from":[43],"sea":[44],"level":[45],"pressure":[46],"(SLP),":[47],"characteristics":[51],"changeability":[53],"complexity.":[55],"With":[56],"applications":[58],"deep":[60,69],"learning":[61],"approaches":[62],"in":[63,241],"field":[65],"climatic":[67],"prediction,":[68],"neural":[70],"networks":[71],"provide":[72],"alternative":[74],"predict":[76,93,103],"beyond":[80],"numerical":[81,250],"models.":[82,251],"In":[83,130,232],"this":[84],"paper,":[85],"long":[86],"short-term":[87],"memory":[88],"(LSTM)":[89],"used":[91,101],"sequence.":[97],"Furthermore,":[98],"ConvLSTM":[99],"SLP":[104,124,163],"grid":[105,164],"data":[106,161,165,173,191],"with":[107,184,245],"capturing":[108],"temporal":[110],"spatial":[112],"interdependencies.":[113],"predicted":[115],"obtained":[120],"projecting":[122],"output":[123],"anomaly":[128],"pattern.":[129],"order":[131],"improve":[133],"accuracy,":[136],"especially":[137],"for":[138,157],"extreme":[139],"events,":[140],"we":[141],"adopt":[142],"discrete":[143],"wavelet":[144],"transform":[145],"(DWT)":[146],"preprocess":[148],"data,":[149],"thus":[150],"DWT-LSTM":[151,223],"DWT-ConvLSTM":[153,225],"models":[154,199,235],"are":[155,200,236],"proposed":[156],"respectively.":[166],"Preprocessing":[167],"steps":[168],"include":[169],"discomposing":[170],"input":[172],"into":[174],"low-frequency":[176],"high-frequency":[179],"components":[180],"using":[181],"DWT":[182],"along":[183],"considering":[185],"local":[187],"time":[188],"dependency.":[189],"Observation":[190],"selected":[193],"as":[194,206],"benchmark,":[196],"our":[198,234],"compared":[201,244],"against":[202],"multiple":[203],"models,":[204],"such":[205],"support":[207],"vector":[208],"regression":[209],"(SVR),":[210],"LSTM,":[211],"gated":[212],"recurrent":[213],"unit":[214],"(GRU)":[215],"etc.":[216],"experimental":[218],"results":[219],"demonstrate":[220],"that":[221],"both":[222],"perform":[226],"better,":[227],"particularly":[228],"at":[229],"peak":[230],"values.":[231],"addition,":[233],"much":[237],"closer":[238],"observation":[240],"multi-step":[242],"forecasting":[243],"ensemble":[246],"forecasts":[247],"based":[248]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
