{"id":"https://openalex.org/W4401009992","doi":"https://doi.org/10.1145/3665053.3665054","title":"Wavelet Based Multiscale Deep Learning Algorithms for Arctic Sea Ice Melting Prediction","display_name":"Wavelet Based Multiscale Deep Learning Algorithms for Arctic Sea Ice Melting Prediction","publication_year":2024,"publication_date":"2024-03-22","ids":{"openalex":"https://openalex.org/W4401009992","doi":"https://doi.org/10.1145/3665053.3665054"},"language":"en","primary_location":{"id":"doi:10.1145/3665053.3665054","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3665053.3665054","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3665053.3665054?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 6th International Symposium on Signal Processing Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3665053.3665054?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106152276","display_name":"Victoria Pegkou Christofi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Victoria Pegkou Christofi","raw_affiliation_strings":["WCSU, USA"],"affiliations":[{"raw_affiliation_string":"WCSU, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060802782","display_name":"Andrew Li","orcid":"https://orcid.org/0009-0007-8713-6574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrew Li","raw_affiliation_strings":["WCSU, USA"],"affiliations":[{"raw_affiliation_string":"WCSU, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102149851","display_name":"Audrey Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Audrey Lin","raw_affiliation_strings":["WCSU, USA"],"affiliations":[{"raw_affiliation_string":"WCSU, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5106152276"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3928,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58808786,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"29","last_page":"39"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11459","display_name":"Arctic and Antarctic ice dynamics","score":0.9993000030517578,"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/T11459","display_name":"Arctic and Antarctic ice dynamics","score":0.9993000030517578,"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/T11333","display_name":"Climate change and permafrost","score":0.9406999945640564,"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/T10995","display_name":"Methane Hydrates and Related Phenomena","score":0.9373000264167786,"subfield":{"id":"https://openalex.org/subfields/2304","display_name":"Environmental Chemistry"},"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/sea-ice","display_name":"Sea ice","score":0.5973448157310486},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.523983895778656},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5163525342941284},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46660348773002625},{"id":"https://openalex.org/keywords/arctic","display_name":"Arctic","score":0.4382181167602539},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4157719016075134},{"id":"https://openalex.org/keywords/arctic-ice-pack","display_name":"Arctic ice pack","score":0.41302162408828735},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3797011375427246},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35434865951538086},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32658764719963074},{"id":"https://openalex.org/keywords/climatology","display_name":"Climatology","score":0.2858635187149048},{"id":"https://openalex.org/keywords/oceanography","display_name":"Oceanography","score":0.2510077953338623}],"concepts":[{"id":"https://openalex.org/C136894858","wikidata":"https://www.wikidata.org/wiki/Q213926","display_name":"Sea ice","level":2,"score":0.5973448157310486},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.523983895778656},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5163525342941284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46660348773002625},{"id":"https://openalex.org/C518008717","wikidata":"https://www.wikidata.org/wiki/Q25322","display_name":"Arctic","level":2,"score":0.4382181167602539},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4157719016075134},{"id":"https://openalex.org/C161798024","wikidata":"https://www.wikidata.org/wiki/Q3651008","display_name":"Arctic ice pack","level":3,"score":0.41302162408828735},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3797011375427246},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35434865951538086},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32658764719963074},{"id":"https://openalex.org/C49204034","wikidata":"https://www.wikidata.org/wiki/Q52139","display_name":"Climatology","level":1,"score":0.2858635187149048},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.2510077953338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3665053.3665054","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3665053.3665054","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3665053.3665054?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 6th International Symposium on Signal Processing Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3665053.3665054","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3665053.3665054","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3665053.3665054?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 6th International Symposium on Signal Processing Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.550000011920929,"display_name":"Life below water","id":"https://metadata.un.org/sdg/14"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401009992.pdf"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W1994522258","https://openalex.org/W2518030137","https://openalex.org/W2608569001","https://openalex.org/W2775070522","https://openalex.org/W2906390219","https://openalex.org/W2944124417","https://openalex.org/W2996318217","https://openalex.org/W3127723844","https://openalex.org/W3139464355","https://openalex.org/W4283022657","https://openalex.org/W4380997829"],"related_works":["https://openalex.org/W2872940795","https://openalex.org/W2902407688","https://openalex.org/W79015229","https://openalex.org/W336147707","https://openalex.org/W4235710270","https://openalex.org/W1485089148","https://openalex.org/W2141422941","https://openalex.org/W2245507316","https://openalex.org/W2034491774","https://openalex.org/W2800370863"],"abstract_inverted_index":{"Sea":[0],"ice":[1,58,250],"has":[2,43],"been":[3,17],"melting":[4],"at":[5],"an":[6,35],"unprecedented":[7],"rate":[8,14],"of":[9,30,56,71,82,91,120,176,218,226,242,284,292],"12.6%":[10],"each":[11],"decade,":[12],"a":[13,27,89,145,187,223,301],"that":[15,42],"hasn't":[16],"observed":[18],"in":[19,61,84,94,211],"over":[20],"1,500":[21],"years":[22],"[16].":[23],"This":[24,272],"shrinkage":[25],"is":[26,34,50],"clear":[28],"signal":[29],"climate":[31,76,279,285,313],"change;":[32],"it":[33,49,245],"urgent":[36],"issue":[37],"directly":[38],"affecting":[39],"Arctic":[40,63,86,116,278],"ecosystems":[41],"global":[44],"implications":[45],"as":[46,124,186,287,289],"well.":[47],"Therefore,":[48],"crucial":[51],"to":[52,64,88,108,114,149,190,247],"utilize":[53],"past":[54],"measurements":[55],"sea":[57,125,249],"coverage":[59],"decline":[60],"the":[62,79,85,115,177,212,216,240,282,305],"create":[65,109],"more":[66],"accurate":[67],"and":[68,136,161,202,205,209,220,228,281],"reliable":[69],"predictions":[70],"future":[72],"decrease.":[73],"However,":[74],"current":[75],"models":[77,112],"underestimate":[78],"rapid":[80],"speed":[81],"warming":[83],"due":[87],"scarcity":[90],"observation,":[92],"resulting":[93],"flawed":[95],"calibration":[96],"[24].":[97],"In":[98,171],"this":[99,295],"research,":[100],"we":[101,142,173,297],"designed":[102],"two":[103],"different":[104,194,264],"neural":[105,151],"network":[106],"architectures":[107],"machine":[110],"learning":[111,147],"tailored":[113],"with":[117,199,215,252,263],"feature":[118],"selection":[119],"specific":[121],"variables":[122],"such":[123],"surface":[126,130,132,137],"temperature,":[127],"total":[128],"precipitation,":[129],"pressure,":[131],"sensible":[133],"heat":[134,139],"flux,":[135],"latent":[138],"flux.":[140],"Further,":[141],"utilized":[143],"TensorFlow,":[144],"deep":[146],"framework":[148],"implement":[150],"networks":[152,166],"including":[153],"Convolutional":[154],"Neural":[155],"Networks":[156],"(CNN)":[157],"for":[158,167,232,312],"image":[159,219],"recognition,":[160],"Long":[162],"Short-Term":[163],"Memory":[164],"(LSTM)":[165],"sequential":[168],"data":[169,192],"analysis.":[170],"particular,":[172],"took":[174],"advantage":[175],"state-of-art":[178],"mathematical":[179],"tool,":[180],"M-band":[181],"Discrete":[182],"Wavelet":[183],"Transforms":[184],"(DWT)":[185],"preproccesing":[188],"step,":[189],"decompose":[191],"into":[193,277],"frequency":[195],"or":[196],"scale":[197],"components":[198],"physically":[200],"meaningful":[201],"interpretable":[203],"features":[204,208,229],"reveal":[206],"hidden":[207],"details":[210],"data.":[213],"Moreover,":[214],"combination":[217],"numerical":[221],"data,":[222,255],"wider":[224],"range":[225],"factors":[227],"are":[230],"integrated":[231],"improved":[233],"model":[234,238,259,265,270],"performance.":[235],"Our":[236],"1D":[237],"demonstrates":[239,260],"importance":[241],"simplicity":[243],"when":[244],"comes":[246],"predicting":[248],"trends":[251],"time":[253],"series":[254],"while":[256],"our":[257],"2D":[258],"how":[261],"experimenting":[262],"configurations":[266],"can":[267],"significantly":[268],"increase":[269],"accuracy.":[271],"innovation":[273],"provides":[274],"deeper":[275],"insight":[276],"behavior":[280],"consequences":[283],"change":[286],"well":[288],"potential":[290],"methods":[291],"mitigation.":[293],"Through":[294],"effort,":[296],"will":[298],"also":[299],"shine":[300],"light":[302],"on":[303],"achieving":[304],"United":[306],"Nations":[307],"2030":[308],"Agenda":[309],"goal":[310],"#13":[311],"action.":[314]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2025-10-10T00:00:00"}