{"id":"https://openalex.org/W7124947005","doi":"https://doi.org/10.1109/icaaic64647.2025.11330661","title":"Seismic Trend Analysis and Prediction","display_name":"Seismic Trend Analysis and Prediction","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7124947005","doi":"https://doi.org/10.1109/icaaic64647.2025.11330661"},"language":"en","primary_location":{"id":"doi:10.1109/icaaic64647.2025.11330661","doi":"https://doi.org/10.1109/icaaic64647.2025.11330661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaaic64647.2025.11330661","pdf_url":null,"source":{"id":"https://openalex.org/S4363607845","display_name":"International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","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 4th International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5123395275","display_name":"Dasari Suma","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":"IN","type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dasari Suma","raw_affiliation_strings":["VIT-AP University,Amaravati"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VIT-AP University,Amaravati","institution_ids":["https://openalex.org/I4401726783"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123385975","display_name":"Akhila Sree Menda","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":"IN","type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Akhila Sree Menda","raw_affiliation_strings":["VIT-AP University,Amaravati"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VIT-AP University,Amaravati","institution_ids":["https://openalex.org/I4401726783"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123435354","display_name":"Boggavarapu Mohitha Sree","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":"IN","type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Boggavarapu Mohitha Sree","raw_affiliation_strings":["VIT-AP University,Amaravati"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"VIT-AP University,Amaravati","institution_ids":["https://openalex.org/I4401726783"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113872480","display_name":"Rajkumar Yesuraj","orcid":"https://orcid.org/0000-0002-2264-7003","observed_orcids":["https://orcid.org/0000-0002-2264-7003"]},"institutions":[{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":"IN","type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rajkumar Yesuraj","raw_affiliation_strings":["SCOPE, VIT-AP University,Amaravati"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SCOPE, VIT-AP University,Amaravati","institution_ids":["https://openalex.org/I4401726783"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4401726783"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"920","last_page":"926"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12424","display_name":"Earthquake Detection and Analysis","score":0.0,"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/T10160","display_name":"Seismic Performance and Analysis","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/earthquake-prediction","display_name":"earthquake prediction","score":0.9129999876022339},{"id":"https://openalex.org/keywords/seismic-event-classification","display_name":"seismic event classification","score":0.7570000290870667},{"id":"https://openalex.org/keywords/large-earthquakes","display_name":"strong earthquakes","score":0.8149999976158142},{"id":"https://openalex.org/keywords/hybrid-deep-learning","display_name":"hybrid deep learning model","score":0.6330000162124634},{"id":"https://openalex.org/keywords/cnn-lstm","display_name":"CNN-LSTM","score":0.9739999771118164},{"id":"https://openalex.org/keywords/cnn-gru","display_name":"CNN-GRU","score":0.9990000128746033},{"id":"https://openalex.org/keywords/spatiotemporal-feature-learning","display_name":"spatiotemporal feature learning","score":0.9470000267028809},{"id":"https://openalex.org/keywords/seismic-sequences","display_name":"seismic sequences","score":0.6729999780654907}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7156999707221985},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6051999926567078},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5667999982833862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4999000132083893},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4959999918937683},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46239998936653137},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4404999911785126},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C11097651","wikidata":"https://www.wikidata.org/wiki/Q606654","display_name":"Earthquake prediction","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C127142870","wikidata":"https://www.wikidata.org/wiki/Q7838279","display_name":"Trend analysis","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.27900001406669617},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2700999975204468}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaaic64647.2025.11330661","doi":"https://doi.org/10.1109/icaaic64647.2025.11330661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaaic64647.2025.11330661","pdf_url":null,"source":{"id":"https://openalex.org/S4363607845","display_name":"International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","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 4th International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1544274373","https://openalex.org/W2064675550","https://openalex.org/W2163605009","https://openalex.org/W2964199361","https://openalex.org/W3047855151","https://openalex.org/W4392235861","https://openalex.org/W4403526076","https://openalex.org/W4408034222"],"related_works":["https://openalex.org/W7127086614","https://openalex.org/W4417404313","https://openalex.org/W4406263188","https://openalex.org/W7163221535","https://openalex.org/W7155983320","https://openalex.org/W4402353628","https://openalex.org/W6949426489","https://openalex.org/W4412869049","https://openalex.org/W4413891815","https://openalex.org/W4406298200"],"abstract_inverted_index":{"Earthquake":[0],"events":[1,13],"pose":[2],"significant":[3,131],"risks":[4],"to":[5,17,31,54],"infrastructure":[6],"and":[7,21,27,84,99],"human":[8],"life,":[9],"yet":[10],"predicting":[11],"large-magnitude":[12],"remains":[14],"difficult":[15],"due":[16],"complex":[18],"geophysical":[19],"behaviour":[20],"uneven":[22],"data":[23],"distribution.":[24],"Traditional":[25],"physics-driven":[26],"probabilistic":[28],"techniques":[29],"struggle":[30],"represent":[32],"key":[33],"spatio-temporal":[34,72],"variations,":[35],"resulting":[36],"in":[37,133],"reduced":[38],"predictive":[39,134],"reliability.":[40],"Recent":[41],"work":[42,110],"shows":[43],"that":[44,155],"deep-learning":[45],"models":[46],"can":[47,159],"better":[48],"capture":[49],"intricate":[50],"seismic":[51,107,172],"patterns":[52],"compared":[53],"classical":[55],"analytical":[56],"methods.":[57],"To":[58],"address":[59],"this":[60],"gap,":[61],"a":[62,97,112,125,130],"novel":[63],"hybrid":[64],"deep":[65,156],"learning":[66,157],"architecture":[67],"is":[68],"designed":[69],"for":[70,106,119,167],"enhanced":[71],"feature":[73,80],"learning.":[74],"This":[75,94],"approach":[76],"combines":[77],"CNN-based":[78],"spatial":[79],"extraction":[81],"with":[82,144,175],"LSTM":[83],"GRU":[85],"temporal":[86],"modelling":[87,105],"layers,":[88],"strengthened":[89],"through":[90],"dense-layer":[91],"classification":[92,123,169],"refinement.":[93],"integration":[95],"enables":[96],"comprehensive":[98],"comparative":[100],"study":[101],"of":[102,115,142,170],"long-term":[103],"dependency":[104],"sequences.":[108],"The":[109,136],"presents":[111],"systematic":[113],"evaluation":[114],"specific":[116],"CNN-temporal":[117],"combinations":[118],"robust":[120],"high-magnitude":[121,171],"event":[122],"on":[124],"standard":[126],"benchmark":[127,179],"dataset,":[128],"demonstrating":[129],"leap":[132],"capability.":[135],"CNN-LSTM":[137],"model":[138],"attained":[139],"an":[140],"accuracy":[141],"97.43%,":[143],"the":[145,162],"CNN-GRU":[146],"variant":[147],"close":[148],"behind":[149],"at":[150],"96.65%.":[151],"These":[152],"findings":[153],"confirm":[154],"architectures":[158],"effectively":[160],"encode":[161],"complex,":[163],"nonlinear":[164],"signals":[165],"required":[166],"accurate":[168],"events,":[173],"consistent":[174],"insights":[176],"from":[177],"prior":[178],"research.":[180]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-01-21T00:00:00"}