{"id":"https://openalex.org/W1547807655","doi":"https://doi.org/10.1109/ijcnn.2005.1556318","title":"Streamflow forecasting using neural networks and fuzzy clustering techniques","display_name":"Streamflow forecasting using neural networks and fuzzy clustering techniques","publication_year":2006,"publication_date":"2006-01-05","ids":{"openalex":"https://openalex.org/W1547807655","doi":"https://doi.org/10.1109/ijcnn.2005.1556318","mag":"1547807655"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2005.1556318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1556318","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","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/A5024822817","display_name":"Ivette Luna","orcid":"https://orcid.org/0000-0002-5304-5523"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"I. Luna","raw_affiliation_strings":["DENSIS-FEEC-UNICAMP, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"DENSIS-FEEC-UNICAMP, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061483642","display_name":"S. Soares","orcid":"https://orcid.org/0000-0002-5621-517X"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"S. Soares","raw_affiliation_strings":["DENSIS-FEEC-UNICAMP, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"DENSIS-FEEC-UNICAMP, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"middle","author":{"id":null,"display_name":"M.H. Magalhaes","orcid":null},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"M.H. Magalhaes","raw_affiliation_strings":["DENSIS-FEEC-UNICAMP, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"DENSIS-FEEC-UNICAMP, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070671093","display_name":"Ros\u00e2ngela Ballini","orcid":"https://orcid.org/0000-0001-6683-4380"},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"R. Ballini","raw_affiliation_strings":["DENSIS-FEEC-UNICAMP, Campinas, Brazil"],"affiliations":[{"raw_affiliation_string":"DENSIS-FEEC-UNICAMP, Campinas, Brazil","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5024822817"],"corresponding_institution_ids":["https://openalex.org/I181391015"],"apc_list":null,"apc_paid":null,"fwci":0.776,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.64081633,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"4","issue":null,"first_page":"2631","last_page":"2636"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9896000027656555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/streamflow","display_name":"Streamflow","score":0.829810380935669},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.674514651298523},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6538598537445068},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6369805335998535},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6092528700828552},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5215216279029846},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.46351301670074463},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.4628545343875885},{"id":"https://openalex.org/keywords/hydroelectricity","display_name":"Hydroelectricity","score":0.4459642469882965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43712693452835083},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4173937141895294},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37419670820236206},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3421788215637207},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20247474312782288},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1745677888393402},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10793697834014893},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0846281349658966}],"concepts":[{"id":"https://openalex.org/C53739315","wikidata":"https://www.wikidata.org/wiki/Q29425295","display_name":"Streamflow","level":3,"score":0.829810380935669},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.674514651298523},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6538598537445068},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6369805335998535},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6092528700828552},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5215216279029846},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.46351301670074463},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.4628545343875885},{"id":"https://openalex.org/C92311004","wikidata":"https://www.wikidata.org/wiki/Q80638","display_name":"Hydroelectricity","level":2,"score":0.4459642469882965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43712693452835083},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4173937141895294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37419670820236206},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3421788215637207},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20247474312782288},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1745677888393402},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10793697834014893},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0846281349658966},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"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/C126645576","wikidata":"https://www.wikidata.org/wiki/Q166620","display_name":"Drainage basin","level":2,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2005.1556318","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1556318","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1512416947","https://openalex.org/W1555984214","https://openalex.org/W1604564480","https://openalex.org/W1923975227","https://openalex.org/W1995946082","https://openalex.org/W1998442441","https://openalex.org/W2113076747","https://openalex.org/W2124776405","https://openalex.org/W2136838997","https://openalex.org/W2153198304","https://openalex.org/W2153694513","https://openalex.org/W2168175751","https://openalex.org/W2285257517","https://openalex.org/W2546315929","https://openalex.org/W2896916590","https://openalex.org/W6630670229","https://openalex.org/W6636310868","https://openalex.org/W6682671693","https://openalex.org/W6729152505"],"related_works":["https://openalex.org/W4238326080","https://openalex.org/W4319979803","https://openalex.org/W4231373790","https://openalex.org/W2393000548","https://openalex.org/W2489724671","https://openalex.org/W173953286","https://openalex.org/W4238625560","https://openalex.org/W4296463726","https://openalex.org/W2439644404","https://openalex.org/W2729426317"],"abstract_inverted_index":{"Planning":[0],"of":[1,49,111],"hydroelectric":[2],"systems":[3],"is":[4,24,80],"a":[5,50,55,74,84,107],"complex":[6],"and":[7,16,54,62,98],"difficult":[8],"task":[9,90],"once":[10],"it":[11],"involves":[12],"non-linear":[13],"production":[14],"characteristics":[15],"depends":[17],"on":[18],"numerous":[19],"variables.":[20],"A":[21],"key":[22],"variable":[23],"the":[25,30,70,112,117],"streamflow.":[26],"Streamflow":[27],"values":[28],"covering":[29],"entire":[31],"planning":[32],"period":[33],"must":[34],"be":[35],"accurately":[36],"forecasted":[37],"because":[38],"they":[39],"strongly":[40],"influence":[41],"energy":[42],"production.":[43],"This":[44],"paper":[45],"suggests":[46],"an":[47],"application":[48],"FIR":[51,113],"neural":[52,86,114],"network":[53,87,115],"fuzzy":[56],"clustering-based":[57],"model":[58,77],"to":[59,69,82,92,100],"evaluate":[60],"one-step":[61],"multi-step":[63],"ahead":[64],"predictions.":[65],"Results":[66],"are":[67],"compared":[68],"ones":[71],"obtained":[72],"by":[73],"periodic":[75],"autoregressive":[76],"(PAR).":[78],"It":[79],"interesting":[81],"apply":[83],"recurrent":[85],"for":[88,95,116],"prediction":[89],"due":[91],"its":[93],"ability":[94],"temporal":[96],"processing":[97],"efficiency":[99],"solve":[101],"nonlinear":[102],"problems.":[103],"The":[104],"results":[105],"show":[106],"generally":[108],"better":[109],"performance":[110],"case":[118],"studied.":[119]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2025-10-10T00:00:00"}
