{"id":"https://openalex.org/W1124005758","doi":null,"title":"Rainfall time series forecasting based on Modular RBF Neural Network model coupled with SSA and PLS","display_name":"Rainfall time series forecasting based on Modular RBF Neural Network model coupled with SSA and PLS","publication_year":2012,"publication_date":"2012-01-01","ids":{"openalex":"https://openalex.org/W1124005758","mag":"1124005758"},"language":"en","primary_location":{"id":"mag:1124005758","doi":null,"is_oa":true,"landing_page_url":"http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-article-BPS3-0025-0117","pdf_url":null,"source":{"id":"https://openalex.org/S2764569393","display_name":"Applied Computer Science","issn_l":"1895-3735","issn":["1895-3735","2353-6977"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","erih-plus","poland-70","russia-white-list-3"],"host_organization":"https://openalex.org/P4310317398","host_organization_name":"Polish Association for Knowledge Promotion","host_organization_lineage":["https://openalex.org/P4310317398"],"host_organization_lineage_names":["Polish Association for Knowledge Promotion"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Applied Computer Science","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-article-BPS3-0025-0117","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086276542","display_name":"Jiansheng Wu Yu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiansheng Wu Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055202852","display_name":"YU Ji-min","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"YU Ji-min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":100,"currency":"EUR","value_usd":109},"apc_paid":{"value":100,"currency":"EUR","value_usd":109},"fwci":1.7855,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.82844046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":97},"biblio":{"volume":"6","issue":"2","first_page":"3","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":1.0,"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":1.0,"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/T10320","display_name":"Neural Networks and Applications","score":9.999999747378752e-05,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/rainfall-prediction","display_name":"precipitation forecasting","score":0.9819999933242798},{"id":"https://openalex.org/keywords/singular-spectrum-analysis","display_name":"singular spectrum analysis","score":0.9980000257492065},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"partial least squares regression","score":0.9120000004768372},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.6330000162124634},{"id":"https://openalex.org/keywords/least-squares-support-vector-regression","display_name":"least squares support vector regression","score":0.9039999842643738},{"id":"https://openalex.org/keywords/time-series-decomposition","display_name":"time series decomposition","score":0.609000027179718},{"id":"https://openalex.org/keywords/flood-management","display_name":"flood management","score":0.7440000176429749},{"id":"https://openalex.org/keywords/real-time-forecasting","display_name":"real-time prediction","score":0.7260000109672546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311598062515259},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6742118000984192},{"id":"https://openalex.org/C98856871","wikidata":"https://www.wikidata.org/wiki/Q1588488","display_name":"Radial basis function","level":3,"score":0.6507564187049866},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.535161554813385},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4661698639392853},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4601387083530426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4548110067844391},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.44456738233566284},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.43100476264953613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3967401683330536}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:1124005758","doi":null,"is_oa":true,"landing_page_url":"http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-article-BPS3-0025-0117","pdf_url":null,"source":{"id":"https://openalex.org/S2764569393","display_name":"Applied Computer Science","issn_l":"1895-3735","issn":["1895-3735","2353-6977"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","erih-plus","poland-70","russia-white-list-3"],"host_organization":"https://openalex.org/P4310317398","host_organization_name":"Polish Association for Knowledge Promotion","host_organization_lineage":["https://openalex.org/P4310317398"],"host_organization_lineage_names":["Polish Association for Knowledge Promotion"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Applied Computer Science","raw_type":null}],"best_oa_location":{"id":"mag:1124005758","doi":null,"is_oa":true,"landing_page_url":"http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.baztech-article-BPS3-0025-0117","pdf_url":null,"source":{"id":"https://openalex.org/S2764569393","display_name":"Applied Computer Science","issn_l":"1895-3735","issn":["1895-3735","2353-6977"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","erih-plus","poland-70","russia-white-list-3"],"host_organization":"https://openalex.org/P4310317398","host_organization_name":"Polish Association for Knowledge Promotion","host_organization_lineage":["https://openalex.org/P4310317398"],"host_organization_lineage_names":["Polish Association for Knowledge Promotion"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Applied Computer Science","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W154790888","https://openalex.org/W1487601331","https://openalex.org/W1496317909","https://openalex.org/W1522922905","https://openalex.org/W1535006492","https://openalex.org/W1560724230","https://openalex.org/W1964448749","https://openalex.org/W1975964493","https://openalex.org/W1982581496","https://openalex.org/W1996150433","https://openalex.org/W2005120494","https://openalex.org/W2029659091","https://openalex.org/W2042272390","https://openalex.org/W2063128958","https://openalex.org/W2067562186","https://openalex.org/W2092436092","https://openalex.org/W2104978738","https://openalex.org/W2107034620","https://openalex.org/W2112020727","https://openalex.org/W2120676854","https://openalex.org/W2151103935","https://openalex.org/W2154301842","https://openalex.org/W2166446427","https://openalex.org/W2174632662","https://openalex.org/W2221700889","https://openalex.org/W2501514717"],"related_works":["https://openalex.org/W291665905","https://openalex.org/W2084678032","https://openalex.org/W2049387654","https://openalex.org/W2051912432","https://openalex.org/W168578057","https://openalex.org/W1522922905","https://openalex.org/W2065044364","https://openalex.org/W2945758510","https://openalex.org/W2021386313","https://openalex.org/W4352991500"],"abstract_inverted_index":{"Accurate":[0],"forecast":[1],"of":[2,7,29,81,92,145,163,244],"rainfall":[3,17,59,179,261],"has":[4],"been":[5],"one":[6],"the":[8,35,45,79,88,99,102,117,120,133,142,151,164,203,215,221,225,235,245,252],"most":[9],"important":[10],"issues":[11],"in":[12,184,191,239,242],"hydrological":[13],"research.":[14],"Due":[15],"to":[16,33,56,140,166,214,260],"forecasting":[18,31,36,60,180],"involves":[19],"a":[20,41,127,192,257],"rather":[21],"complex":[22,93],"nonlinear":[23],"data":[24,103],"pattern;":[25],"there":[26],"are":[27,124,228],"lots":[28],"novel":[30],"approaches":[32],"improve":[34],"accuracy.":[37],"In":[38,78,98,116,132,150],"this":[39,240],"paper,":[40],"new":[42],"approach":[43,227],"using":[44,224,234],"Modular":[46],"Radial":[47],"Basis":[48],"Function":[49],"Neural":[50],"Network":[51],"(M-RBF-NN)":[52],"technique":[53,255],"is":[54,85,105,138,159,173],"presented":[55,238],"im-":[57],"prove":[58],"performance":[61],"coupled":[62],"with":[63,202],"appropriate":[64,143],"data-preprocessing":[65],"techniques":[66],"by":[67,111,126,209],"Singular":[68],"Spectrum":[69],"Analysis":[70],"(SSA)":[71],"and":[72,95,113,181],"Partial":[73],"Least":[74],"Square":[75],"(PLS)":[76],"regression.":[77],"process":[80],"modular":[82,121],"modeling,":[83],"SSA":[84],"applied":[86,175],"for":[87,161,176],"time":[89,178,195],"series":[90],"extraction":[91],"trends":[94],"structure":[96],"finding.":[97],"second":[100],"stage,":[101,119,135,153],"set":[104],"divided":[106],"into":[107],"different":[108,197],"training":[109],"sets":[110],"Bagging":[112],"Boosting":[114],"technology.":[115],"third":[118],"RBF-NN":[122,171],"predictors":[123],"produced":[125],"differ-":[128],"ent":[129],"kernel":[130],"function.":[131],"fourth":[134],"PLS":[136],"technology":[137],"used":[139,160],"choose":[141],"number":[144],"neural":[146],"network":[147,198],"ensemble":[148,162],"members.":[149],"final":[152],"least":[154],"squares":[155],"support":[156],"vector":[157],"regression":[158],"M-RBF-NN":[165,210,226,254],"prediction":[167],"purpose.":[168],"The":[169],"developed":[170],"model":[172,211],"being":[174],"real":[177,194],"flood":[182],"management":[183],"Liuzhou,":[185],"Guangxi.":[186],"Aimed":[187],"at":[188],"providing":[189],"forecasts":[190,208],"near":[193],"schedule,":[196],"types":[199],"were":[200,212],"tested":[201],"same":[204,246],"input":[205],"information.":[206],"Additionally,":[207],"compared":[213],"convenient":[216],"approach.":[217],"Results":[218],"show":[219],"that":[220,251],"predictions":[222],"made":[223],"consistently":[229],"better":[230],"than":[231],"those":[232],"obtained":[233],"other":[236],"method":[237],"study":[241],"terms":[243],"measurements.":[247],"Sensitivity":[248],"analysis":[249],"indicated":[250],"proposed":[253],"provides":[256],"promising":[258],"alternative":[259],"prediction.":[262]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}