{"id":"https://openalex.org/W1522195872","doi":"https://doi.org/10.1109/pimrc.2004.1368831","title":"Simulation of rain events time series with Markov model","display_name":"Simulation of rain events time series with Markov model","publication_year":2005,"publication_date":"2005-01-12","ids":{"openalex":"https://openalex.org/W1522195872","doi":"https://doi.org/10.1109/pimrc.2004.1368831","mag":"1522195872"},"language":"en","primary_location":{"id":"doi:10.1109/pimrc.2004.1368831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2004.1368831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754)","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/A5015025894","display_name":"C. AIasseur","orcid":null},"institutions":[{"id":"https://openalex.org/I102475099","display_name":"Sup\u00e9lec","ror":"https://ror.org/00n7gwn90","country_code":"FR","type":"education","lineage":["https://openalex.org/I102475099"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"C. AIasseur","raw_affiliation_strings":["Supelec, Gif-sur-Yvette, France","SUPELEC - Gif sur Yvette, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Supelec, Gif-sur-Yvette, France","institution_ids":["https://openalex.org/I102475099"]},{"raw_affiliation_string":"SUPELEC - Gif sur Yvette, France","institution_ids":["https://openalex.org/I102475099"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028357817","display_name":"L. Husson","orcid":null},"institutions":[{"id":"https://openalex.org/I102475099","display_name":"Sup\u00e9lec","ror":"https://ror.org/00n7gwn90","country_code":"FR","type":"education","lineage":["https://openalex.org/I102475099"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"L. Husson","raw_affiliation_strings":["Sup\u00e9lec, Gif-sur-Yvette, France","SUPELEC - Gif sur Yvette, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sup\u00e9lec, Gif-sur-Yvette, France","institution_ids":["https://openalex.org/I102475099"]},{"raw_affiliation_string":"SUPELEC - Gif sur Yvette, France","institution_ids":["https://openalex.org/I102475099"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015561437","display_name":"F. P\u00e9rez\u2010Font\u00e1n","orcid":"https://orcid.org/0000-0002-0783-7562"},"institutions":[{"id":"https://openalex.org/I6289922","display_name":"Universidade de Vigo","ror":"https://ror.org/05rdf8595","country_code":"ES","type":"education","lineage":["https://openalex.org/I6289922"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"F. Perez-Fontan","raw_affiliation_strings":["Universidad de Vigo ETSE Telecomunicaci\u00f3n, Campus Universitario, Vigo, Spain","Univ. of Vigo#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad de Vigo ETSE Telecomunicaci\u00f3n, Campus Universitario, Vigo, Spain","institution_ids":["https://openalex.org/I6289922"]},{"raw_affiliation_string":"Univ. of Vigo#TAB#","institution_ids":["https://openalex.org/I6289922"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2801","last_page":"2805"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":0.43290001153945923,"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"}},"topics":[{"id":"https://openalex.org/T11234","display_name":"Precipitation Measurement and Analysis","score":0.43290001153945923,"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"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.3765000104904175,"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/computer-science","display_name":"Computer science","score":0.6270058155059814},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6151578426361084},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5603675246238708},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5248668193817139},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5213329195976257},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.5062788724899292},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24073085188865662},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1918487846851349},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15968242287635803},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.08800554275512695}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6270058155059814},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6151578426361084},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5603675246238708},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5248668193817139},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5213329195976257},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.5062788724899292},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24073085188865662},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1918487846851349},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15968242287635803},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.08800554275512695},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/pimrc.2004.1368831","is_oa":false,"landing_page_url":"https://doi.org/10.1109/pimrc.2004.1368831","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322138","display_name":"Universidad Polit\u00e9cnica de Madrid","ror":"https://ror.org/03n6nwv02"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W87038399","https://openalex.org/W104228611","https://openalex.org/W124464760","https://openalex.org/W315598820","https://openalex.org/W1635803162","https://openalex.org/W2125838338","https://openalex.org/W6603520957","https://openalex.org/W6611055298"],"related_works":["https://openalex.org/W2379651310","https://openalex.org/W2113019827","https://openalex.org/W1541249122","https://openalex.org/W2084326697","https://openalex.org/W2027903142","https://openalex.org/W2354322608","https://openalex.org/W2804608325","https://openalex.org/W2077211377","https://openalex.org/W2186675474","https://openalex.org/W2387462590"],"abstract_inverted_index":{"This":[0,46,99],"work":[1],"presents":[2],"a":[3,11,26,57,61],"rain":[4,65,85,93,104,120],"rate":[5,66],"time":[6],"series":[7],"model":[8,14,54,77,101,128],"based":[9,78],"on":[10,79],"two-level":[12,100],"Markov":[13,76],"structure.":[15],"'Rain'":[16],"or":[17,31],"'no-rain'":[18],"events":[19,121],"are":[20,68],"generated":[21,69],"in":[22,25,122],"two":[23],"steps:":[24],"first":[27,50],"time,":[28,63],"the":[29,35,42,49,53,64,80,88,91,96,113,123,127],"'rain'":[30],"'inter-rain'":[32],"duration":[33],"of":[34,52,83,90,112],"considered":[36],"event":[37],"is":[38,56,129],"determined":[39],"according":[40],"to":[41,48,71],"experimental":[43,84,114],"data":[44,115],"series.":[45],"corresponds":[47],"level":[51],"that":[55],"semi-Markov":[58],"model.":[59],"In":[60],"second":[62],"intensities":[67],"resorting":[70],"an":[72],"N":[73],"states":[74],"hidden":[75],"conditional":[81],"probabilities":[82],"samples":[86,105],"(modeling":[87],"dependence":[89],"current":[92],"intensity":[94],"with":[95],"previous":[97],"one).":[98],"produces":[102],"simulated":[103],"whose":[106],"statistics":[107],"fit":[108],"very":[109],"accurately":[110],"those":[111],"without":[116],"using":[117],"any":[118],"stored":[119],"generation":[124],"step":[125],"when":[126],"known.":[130]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":3},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}