{"id":"https://openalex.org/W7064968019","doi":null,"title":"Detecting climate patterns: Bayesian neural network approach","display_name":"Detecting climate patterns: Bayesian neural network approach","publication_year":2022,"publication_date":"2022-04-26","ids":{"openalex":"https://openalex.org/W7064968019"},"language":"en","primary_location":{"id":"pmh:oai:tudelft.nl:uuid:cfb96794-23a1-4c44-8bc7-84c6677d3f76","doi":null,"is_oa":false,"landing_page_url":"http://resolver.tudelft.nl/uuid:cfb96794-23a1-4c44-8bc7-84c6677d3f76","pdf_url":null,"source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"master thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"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":"Arens, Jesse (author)","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Arens, Jesse (author)","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.9750999808311462,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T10029","display_name":"Climate variability and models","score":0.9750999808311462,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0066999997943639755,"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.0031999999191612005,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-neural-networks","display_name":"Bayesian neural networks","score":0.8849999904632568},{"id":"https://openalex.org/keywords/climate-change-detection","display_name":"climate change detection","score":0.7789999842643738},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"uncertainty quantification","score":0.9610000252723694},{"id":"https://openalex.org/keywords/climate-simulation","display_name":"climate model simulation","score":0.7390000224113464},{"id":"https://openalex.org/keywords/temperature-change","display_name":"temperature change","score":0.6299999952316284},{"id":"https://openalex.org/keywords/precipitation","display_name":"precipitation","score":0.7360000014305115},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.6949999928474426},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.7689999938011169}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7634000182151794},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6290000081062317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6014000177383423},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6011999845504761},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5910999774932861},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5320000052452087},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3400000035762787},{"id":"https://openalex.org/C42683663","wikidata":"https://www.wikidata.org/wiki/Q3517834","display_name":"Mean radiant temperature","level":3,"score":0.33000001311302185},{"id":"https://openalex.org/C107054158","wikidata":"https://www.wikidata.org/wiki/Q25257","display_name":"Precipitation","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C168754636","wikidata":"https://www.wikidata.org/wiki/Q620920","display_name":"Climate model","level":3,"score":0.313400000333786},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3003999888896942},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C134342201","wikidata":"https://www.wikidata.org/wiki/Q7246859","display_name":"Probabilistic neural network","level":4,"score":0.28220000863075256},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.2680000066757202},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.2630999982357025},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.25929999351501465},{"id":"https://openalex.org/C39820584","wikidata":"https://www.wikidata.org/wiki/Q125928","display_name":"Climate pattern","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:tudelft.nl:uuid:cfb96794-23a1-4c44-8bc7-84c6677d3f76","doi":null,"is_oa":false,"landing_page_url":"http://resolver.tudelft.nl/uuid:cfb96794-23a1-4c44-8bc7-84c6677d3f76","pdf_url":null,"source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"master thesis"}],"best_oa_location":null,"sustainable_development_goals":[{"score":1.0,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3029338315","https://openalex.org/W2981924735","https://openalex.org/W2991419155","https://openalex.org/W152970461","https://openalex.org/W4404049225","https://openalex.org/W4296773048","https://openalex.org/W2792027551","https://openalex.org/W4412834665","https://openalex.org/W4321495196","https://openalex.org/W4400811663"],"abstract_inverted_index":{"Machine":[0],"learning":[1,20],"is":[2,29,92,102],"becoming":[3],"an":[4,70],"increasingly":[5],"important":[6],"tool":[7,139],"for":[8,22,85,128,140],"climate":[9,28,43,116],"scientists,":[10],"but":[11,87],"hampered":[12],"by":[13],"lacking":[14],"uncertainty":[15,36],"quantification.":[16],"Here,":[17],"a":[18,26,76,137,146],"machine":[19],"approach":[21],"detecting":[23],"patterns":[24,117,123,144],"indicating":[25,145],"changing":[27,147],"combined":[30],"with":[31,75],"probabilistic":[32],"modelling":[33],"to":[34,69,94],"retrieve":[35],"values.":[37],"We":[38,55],"train":[39],"neural":[40,62,72],"networks":[41],"on":[42],"model":[44],"simulations":[45],"of":[46,82,143],"temperature":[47,96],"and":[48,52],"precipitation":[49],"under":[50],"historical":[51],"future":[53],"scenarios.":[54],"find":[56],"that":[57,112,134],"the":[58,107,122],"resulting":[59],"so-called":[60],"Bayesian":[61],"network":[63,73],"(BNN)":[64],"has":[65],"similar":[66],"predictive":[67],"strength":[68],"Artificial":[71],"(ANN),":[74],"post-year":[77],"2000":[78],"mean":[79],"absolute":[80],"error":[81],"9.00":[83],"years":[84,104],"temperature,":[86],"over-fits":[88],"less.":[89],"The":[90],"BNN":[91],"able":[93],"recognise":[95],"change":[97],"starting":[98],"in":[99,114],"1994,":[100],"which":[101],"14":[103],"later":[105],"than":[106,121],"ANN.":[108],"Our":[109],"analysis":[110],"shows":[111],"uncertainties":[113,142],"found":[115],"are":[118,136],"much":[119],"higher":[120],"themselves,":[124],"reducing":[125],"their":[126],"value":[127],"further":[129],"use.":[130],"This":[131],"work":[132],"demonstrates":[133],"BNNs":[135],"suitable":[138],"quantifying":[141],"climate.":[148]},"counts_by_year":[],"updated_date":"2026-02-09T06:03:36.439944","created_date":"2025-10-10T00:00:00"}