{"id":"https://openalex.org/W2125266796","doi":"https://doi.org/10.1109/icsmc.2008.4811589","title":"Hybrid forecasting method of GM(1,1) disaster model with application to regional ggain production","display_name":"Hybrid forecasting method of GM(1,1) disaster model with application to regional ggain production","publication_year":2008,"publication_date":"2008-10-01","ids":{"openalex":"https://openalex.org/W2125266796","doi":"https://doi.org/10.1109/icsmc.2008.4811589","mag":"2125266796"},"language":"en","primary_location":{"id":"doi:10.1109/icsmc.2008.4811589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2008.4811589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Systems, Man and Cybernetics","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/A5101530081","display_name":"Bingjun Li","orcid":"https://orcid.org/0000-0002-6239-4127"},"institutions":[{"id":"https://openalex.org/I4750791","display_name":"Henan Agricultural University","ror":"https://ror.org/04eq83d71","country_code":"CN","type":"education","lineage":["https://openalex.org/I4750791"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Bing-jun Li","raw_affiliation_strings":["College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China","Coll. of Inf. & Manage. Sci., Henan Agric. Univ., Zhengzhou"],"affiliations":[{"raw_affiliation_string":"College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China","institution_ids":["https://openalex.org/I4750791"]},{"raw_affiliation_string":"Coll. of Inf. & Manage. Sci., Henan Agric. Univ., Zhengzhou","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075050549","display_name":"Masahiro Inuiguchi","orcid":"https://orcid.org/0000-0001-9970-1621"},"institutions":[{"id":"https://openalex.org/I4210111835","display_name":"Osaka Health Science University","ror":"https://ror.org/01tvqd679","country_code":"JP","type":"education","lineage":["https://openalex.org/I4210111835"]},{"id":"https://openalex.org/I98285908","display_name":"Osaka University","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahiro Inuiguchi","raw_affiliation_strings":["Graduate School of Engineering Science Osaka University, Osaka, Japan","Grad. Sch. of Eng. Sci., Osaka Univ., Osaka"],"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering Science Osaka University, Osaka, Japan","institution_ids":["https://openalex.org/I4210111835","https://openalex.org/I98285908"]},{"raw_affiliation_string":"Grad. Sch. of Eng. Sci., Osaka Univ., Osaka","institution_ids":["https://openalex.org/I98285908"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101530081"],"corresponding_institution_ids":["https://openalex.org/I4750791"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17978173,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"2028","last_page":"2033"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6317039132118225},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5612176060676575},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.5341678261756897},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5299863815307617},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4938815236091614},{"id":"https://openalex.org/keywords/time-sequence","display_name":"Time sequence","score":0.4779655635356903},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4648199677467346},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.45952826738357544},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4453846514225006},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.4319073259830475},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42166274785995483},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4113958179950714},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28316211700439453},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.25752177834510803},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19285091757774353}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6317039132118225},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5612176060676575},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.5341678261756897},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5299863815307617},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4938815236091614},{"id":"https://openalex.org/C3020136221","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time sequence","level":2,"score":0.4779655635356903},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4648199677467346},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.45952826738357544},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4453846514225006},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.4319073259830475},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42166274785995483},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4113958179950714},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28316211700439453},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.25752177834510803},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19285091757774353},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsmc.2008.4811589","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsmc.2008.4811589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Conference on Systems, Man and Cybernetics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.800000011920929,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2375928952","https://openalex.org/W3148553227"],"related_works":["https://openalex.org/W31220157","https://openalex.org/W63065332","https://openalex.org/W1990738922","https://openalex.org/W3093365844","https://openalex.org/W2288557197","https://openalex.org/W2983071886","https://openalex.org/W4233024177","https://openalex.org/W397719029","https://openalex.org/W2023538514","https://openalex.org/W4283650582"],"abstract_inverted_index":{"Each":[0],"technique":[1],"has":[2],"its":[3],"own":[4],"drawback":[5],"and":[6,49,74,106,135,139,163],"advantage.":[7],"There":[8],"is":[9,13,27,52,63],"no":[10],"method":[11],"that":[12],"powerful":[14],"in":[15],"any":[16],"problems.":[17],"Therefore,":[18],"the":[19,31,34,56,60,79,113,118,145,152,160,166],"hybridization":[20],"of":[21,33,154,165],"two":[22],"or":[23],"more":[24],"different":[25],"techniques":[26],"important":[28],"to":[29,151],"overcome":[30],"disadvantages":[32],"individual":[35],"techniques.":[36],"In":[37],"this":[38],"paper,":[39],"a":[40,44,88,97,107],"data":[41,61,73,105],"sequence":[42,62],"having":[43],"linear":[45,57,108,146],"tendency":[46],"with":[47],"upper/positive":[48,68],"lower/negative":[50,71],"aberrances":[51],"analyzed.":[53],"Based":[54],"on":[55,93,102,112],"regression":[58,109,147],"analysis,":[59,83],"classified":[64],"into":[65],"three":[66,86],"parts:":[67],"aberrant":[69,72,95,104,123,128,137],"data,":[70,96,138],"normal":[75,115,140],"data.":[76,116],"Then":[77],"introducing":[78],"grey":[80],"disaster":[81,90,99],"forecast":[82],"we":[84,121,158],"establish":[85],"models:":[87],"GM(1,1)":[89,98,132],"model":[91,100,110],"based":[92,101,111],"upper":[94,134],"lower":[103,136],"remaining":[114],"Using":[117],"established":[119],"models,":[120],"obtain":[122],"forecasting":[124,141],"values":[125],"at":[126],"oncoming":[127],"time":[129],"points":[130],"by":[131,144],"from":[133],"value":[142],"obtained":[143],"function.":[148],"Applying":[149],"it":[150],"prediction":[153],"regional":[155],"grain":[156],"production,":[157],"demonstrate":[159],"good":[161],"performance":[162],"effectiveness":[164],"proposed":[167],"hybrid":[168],"method.":[169]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}