{"id":"https://openalex.org/W4411584635","doi":"https://doi.org/10.1109/aemcse65292.2025.11042301","title":"Short-term Prediction of Ionospheric TEC Based on SSA-BP and LSTM","display_name":"Short-term Prediction of Ionospheric TEC Based on SSA-BP and LSTM","publication_year":2025,"publication_date":"2025-05-09","ids":{"openalex":"https://openalex.org/W4411584635","doi":"https://doi.org/10.1109/aemcse65292.2025.11042301"},"language":"en","primary_location":{"id":"doi:10.1109/aemcse65292.2025.11042301","doi":"https://doi.org/10.1109/aemcse65292.2025.11042301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aemcse65292.2025.11042301","pdf_url":null,"source":{"id":"https://openalex.org/S4306498772","display_name":"International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 8th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","raw_type":"proceedings-article"},"volume":null,"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":null,"display_name":"Runze Ni","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Runze Ni","raw_affiliation_strings":["China Research Institute of Radio Wave Propagation,Qingdao,Shandong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Research Institute of Radio Wave Propagation,Qingdao,Shandong,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100450620","display_name":"Wenchao Zhang","orcid":"https://orcid.org/0000-0002-1449-3067","observed_orcids":["https://orcid.org/0000-0002-1449-3067"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenchao Zhang","raw_affiliation_strings":["China Research Institute of Radio Wave Propagation,Qingdao,Shandong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Research Institute of Radio Wave Propagation,Qingdao,Shandong,China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Shuji Hao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuji Hao","raw_affiliation_strings":["China Research Institute of Radio Wave Propagation,Qingdao,Shandong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Research Institute of Radio Wave Propagation,Qingdao,Shandong,China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.5551,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.94325964,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"246","last_page":"249"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10159","display_name":"Ionosphere and magnetosphere dynamics","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10159","display_name":"Ionosphere and magnetosphere dynamics","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12424","display_name":"Earthquake Detection and Analysis","score":0.004800000227987766,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"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":0.0008999999845400453,"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/ionospheric-total-electron-content","display_name":"ionospheric total electron content","score":0.9869999885559082},{"id":"https://openalex.org/keywords/tec-prediction","display_name":"TEC prediction","score":0.8080000281333923},{"id":"https://openalex.org/keywords/geomagnetic-storms","display_name":"geomagnetic storms","score":0.7509999871253967},{"id":"https://openalex.org/keywords/sparrow-search-algorithm","display_name":"sparrow search algorithm","score":0.9409999847412109},{"id":"https://openalex.org/keywords/back-propagation-neural-network","display_name":"BP neural network","score":0.9290000200271606},{"id":"https://openalex.org/keywords/long-short-term-memory-networks","display_name":"long short-term memory networks","score":0.9490000009536743},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.8370000123977661},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"reinforcement learning","score":0.722000002861023},{"id":"https://openalex.org/keywords/hybrid-deep-learning","display_name":"hybrid deep learning model","score":0.902999997138977},{"id":"https://openalex.org/keywords/space-weather","display_name":"space weather","score":0.593999981880188}],"concepts":[{"id":"https://openalex.org/C176379880","wikidata":"https://www.wikidata.org/wiki/Q18031939","display_name":"TEC","level":3,"score":0.9424410462379456},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.6834980845451355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5997120141983032},{"id":"https://openalex.org/C116403925","wikidata":"https://www.wikidata.org/wiki/Q162219","display_name":"Ionosphere","level":2,"score":0.5964673757553101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3835592269897461},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2765287160873413},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.21376994252204895},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.07984083890914917},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05720645189285278}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aemcse65292.2025.11042301","doi":"https://doi.org/10.1109/aemcse65292.2025.11042301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aemcse65292.2025.11042301","pdf_url":null,"source":{"id":"https://openalex.org/S4306498772","display_name":"International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 8th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":6,"referenced_works":["https://openalex.org/W1907127744","https://openalex.org/W1998983070","https://openalex.org/W2804344511","https://openalex.org/W3013592816","https://openalex.org/W3092053701","https://openalex.org/W4396684768"],"related_works":["https://openalex.org/W2770061914","https://openalex.org/W2170328996","https://openalex.org/W2550010074","https://openalex.org/W1989070650","https://openalex.org/W4377992958","https://openalex.org/W4211181610","https://openalex.org/W1992137669","https://openalex.org/W2388413386","https://openalex.org/W2005737353","https://openalex.org/W1994463517"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,5,13,33,71,97,102,124,130,157],"problem":[3],"that":[4,85],"prediction":[6,56],"accuracy":[7],"is":[8,29,58,117,146],"greatly":[9],"reduced":[10],"due":[11],"to":[12,31,69],"irregular":[14],"disturbance":[15],"of":[16,89,101,123,132,150,156],"ionospheric":[17,54],"TEC":[18,55,72],"during":[19],"magnetic":[20,90],"storm,":[21],"a":[22,50],"Q-Learning":[23],"algorithm":[24],"based":[25],"on":[26],"reinforcement":[27],"learning":[28,53],"proposed":[30],"optimize":[32],"Sparrow":[34],"Search":[35],"Algorithm":[36],"Optimized":[37],"BP":[38],"neural":[39],"network":[40],"model":[41,57,62,104],"and":[42,48,63,95,113,143],"Long":[43],"Short-Term":[44],"Memory":[45],"Network":[46],"model,":[47],"then":[49],"combined":[51,61,103],"deep":[52],"established.":[59],"The":[60,82],"two":[64,125,158],"single":[65,126,159],"models":[66],"are":[67,109],"used":[68],"forecast":[70,105,133],"data":[73],"in":[74],"China":[75],"provided":[76],"by":[77],"CODE":[78],"for":[79,106],"three":[80,107],"days.":[81],"results":[83],"show":[84],"under":[86],"different":[87],"levels":[88],"storms":[91],"(strong,":[92],"medium,":[93],"weak,":[94],"none),":[96],"average":[98,148],"relative":[99],"accuracies":[100],"days":[108],"95.9%,":[110],"95.7%,":[111],"92.6%,":[112],"95.3%,":[114],"respectively,":[115],"which":[116,145],"about":[118,151],"6%":[119],"higher":[120],"than":[121,136],"these":[122,155],"models.":[127,160],"Among":[128],"them,":[129],"proportion":[131],"residuals":[134],"less":[135],"1":[137],"TECu":[138],"reaches":[139],"60%,":[140],"59%,":[141],"76%":[142],"98%,":[144],"an":[147],"increase":[149],"27%":[152],"compared":[153],"with":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-10-05T09:50:58.205359","created_date":"2025-10-10T00:00:00"}