{"id":"https://openalex.org/W1983819116","doi":"https://doi.org/10.1109/icnc.2012.6234704","title":"A hybrid neural network model for sea ice thickness forecasting","display_name":"A hybrid neural network model for sea ice thickness forecasting","publication_year":2012,"publication_date":"2012-05-01","ids":{"openalex":"https://openalex.org/W1983819116","doi":"https://doi.org/10.1109/icnc.2012.6234704","mag":"1983819116"},"language":"en","primary_location":{"id":"doi:10.1109/icnc.2012.6234704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2012.6234704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 8th International Conference on Natural Computation","raw_type":"proceedings-article"},"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/A5082565489","display_name":"Hong Lin","orcid":"https://orcid.org/0000-0003-1534-8299"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Lin","raw_affiliation_strings":["College of Pipeline and Civil Engineering, China University of Petroleum, Qingdao, China","College of Pipeline and civil engineering, China University of Petroleum, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Pipeline and Civil Engineering, China University of Petroleum, Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]},{"raw_affiliation_string":"College of Pipeline and civil engineering, China University of Petroleum, Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014885501","display_name":"Lei Yang","orcid":"https://orcid.org/0000-0002-1439-9134"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Yang","raw_affiliation_strings":["College of Science, China University of Petroleum, Qingdao, China","[College of Science China University of Petroleum, Qingdao, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Science, China University of Petroleum, Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]},{"raw_affiliation_string":"[College of Science China University of Petroleum, Qingdao, China]","institution_ids":["https://openalex.org/I4210162190"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210162190"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"358","last_page":"361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12682","display_name":"Smart Materials for Construction","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"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/T12682","display_name":"Smart Materials for Construction","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9801999926567078,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12169","display_name":"Non-Destructive Testing Techniques","score":0.970300018787384,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7042640447616577},{"id":"https://openalex.org/keywords/weibull-distribution","display_name":"Weibull distribution","score":0.626614511013031},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.6079043745994568},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5713835954666138},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.47862333059310913},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3004167675971985},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24481800198554993},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10774129629135132}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7042640447616577},{"id":"https://openalex.org/C173291955","wikidata":"https://www.wikidata.org/wiki/Q732332","display_name":"Weibull distribution","level":2,"score":0.626614511013031},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.6079043745994568},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5713835954666138},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.47862333059310913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3004167675971985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24481800198554993},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10774129629135132},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icnc.2012.6234704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icnc.2012.6234704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 8th International Conference on Natural Computation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life below water","id":"https://metadata.un.org/sdg/14","score":0.6200000047683716}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W101663867","https://openalex.org/W1500731913","https://openalex.org/W1992332319","https://openalex.org/W1994157250","https://openalex.org/W2067562186","https://openalex.org/W3214074647","https://openalex.org/W4302081184"],"related_works":["https://openalex.org/W2372415543","https://openalex.org/W2354205711","https://openalex.org/W2808717917","https://openalex.org/W2377292223","https://openalex.org/W2366584243","https://openalex.org/W2376563992","https://openalex.org/W2360006733","https://openalex.org/W4304590249","https://openalex.org/W2366368367","https://openalex.org/W2385996327"],"abstract_inverted_index":{"Sea":[0,79],"ice":[1,22,33,75,120,126],"thickness":[2,34,76,121,127],"is":[3,25,53,72,80,92,109,122,136],"an":[4],"important":[5],"environment":[6,141],"load":[7,131,142],"parameter":[8],"for":[9,119,147],"reliability":[10],"assessment":[11,130],"and":[12,40,62,88,97,111,138],"life":[13],"extension":[14],"decision":[15],"of":[16,115],"existing":[17],"ageing":[18,148],"offshore":[19],"platforms":[20],"in":[21,77,132],"region.":[23],"It":[24],"a":[26,67,89],"key":[27],"factor":[28],"to":[29],"provide":[30],"accurate":[31],"sea":[32],"prediction.":[35],"Introducing":[36],"chaos":[37,57],"random":[38],"sequence":[39],"immune":[41,49,58],"mechanism":[42],"into":[43],"traditional":[44],"genetic":[45,50,59],"evolution":[46],"process,":[47],"Chaos":[48],"optimization":[51,60],"algorithm":[52,61],"constructed.":[54],"Combining":[55],"the":[56,63,83,95,104],"BP":[64],"neural":[65,69,85,106],"network,":[66],"hybrid":[68,84,105],"network":[70,86,107],"models":[71],"established.":[73],"The":[74,100,113,129],"Bohai":[78],"predicted":[81,125],"using":[82,124],"model,":[87],"good":[90],"fitness":[91],"revealed":[93],"between":[94],"prediction":[96],"practical":[98],"values.":[99],"results":[101],"show":[102],"that":[103],"model":[108],"feasible":[110],"effective.":[112],"parameters":[114,143],"Weibull":[116],"distribution":[117],"function":[118],"estimated,":[123],"specimens.":[128],"later":[133],"service":[134],"period":[135],"updated":[137],"more":[139],"reliable":[140],"can":[144],"be":[145],"provided":[146],"platform":[149],"assessment.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
