{"id":"https://openalex.org/W2072132808","doi":"https://doi.org/10.2478/v10006-012-0062-1","title":"A rainfall forecasting method using machine learning models and its application to the Fukuoka city case","display_name":"A rainfall forecasting method using machine learning models and its application to the Fukuoka city case","publication_year":2012,"publication_date":"2012-12-28","ids":{"openalex":"https://openalex.org/W2072132808","doi":"https://doi.org/10.2478/v10006-012-0062-1","mag":"2072132808"},"language":"en","primary_location":{"id":"doi:10.2478/v10006-012-0062-1","is_oa":true,"landing_page_url":"https://doi.org/10.2478/v10006-012-0062-1","pdf_url":"https://sciendo.com/pdf/10.2478/v10006-012-0062-1","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://sciendo.com/pdf/10.2478/v10006-012-0062-1","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113529923","display_name":"Sirajummonira Sumi","orcid":null},"institutions":[{"id":"https://openalex.org/I207014233","display_name":"Kyushu Institute of Technology","ror":"https://ror.org/02278tr80","country_code":"JP","type":"education","lineage":["https://openalex.org/I207014233"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"S. Monira Sumi","raw_affiliation_strings":["Kyushu Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyushu Institute of Technology","institution_ids":["https://openalex.org/I207014233"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111416671","display_name":"M. Faisal Zaman","orcid":null},"institutions":[{"id":"https://openalex.org/I42934936","display_name":"Dublin City University","ror":"https://ror.org/04a1a1e81","country_code":"IE","type":"education","lineage":["https://openalex.org/I42934936"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"M. Faisal Zaman","raw_affiliation_strings":["Dublin City University#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dublin City University#TAB#","institution_ids":["https://openalex.org/I42934936"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064933175","display_name":"Hideo Hirose","orcid":"https://orcid.org/0000-0002-1752-6063"},"institutions":[{"id":"https://openalex.org/I207014233","display_name":"Kyushu Institute of Technology","ror":"https://ror.org/02278tr80","country_code":"JP","type":"education","lineage":["https://openalex.org/I207014233"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideo Hirose","raw_affiliation_strings":["Kyushu Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kyushu Institute of Technology","institution_ids":["https://openalex.org/I207014233"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":4080,"currency":"PLN","value_usd":1100},"apc_paid":{"value":4080,"currency":"PLN","value_usd":1100},"fwci":1.8517,"has_fulltext":true,"cited_by_count":106,"citation_normalized_percentile":{"value":0.84116238,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"22","issue":"4","first_page":"841","last_page":"854"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9994999766349792,"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":0.9994999766349792,"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/T11186","display_name":"Hydrology and Drought Analysis","score":0.9851999878883362,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.983299970626831,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6727498173713684},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5952889919281006},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5889528393745422},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5127013921737671},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4404631555080414},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4215264916419983},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4163201153278351},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4125930368900299}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6727498173713684},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5952889919281006},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5889528393745422},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5127013921737671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4404631555080414},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4215264916419983},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4163201153278351},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4125930368900299}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.2478/v10006-012-0062-1","is_oa":true,"landing_page_url":"https://doi.org/10.2478/v10006-012-0062-1","pdf_url":"https://sciendo.com/pdf/10.2478/v10006-012-0062-1","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.390.6538","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.390.6538","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://matwbn.icm.edu.pl/ksiazki/amc/amc22/amc2245.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.2478/v10006-012-0062-1","is_oa":true,"landing_page_url":"https://doi.org/10.2478/v10006-012-0062-1","pdf_url":"https://sciendo.com/pdf/10.2478/v10006-012-0062-1","source":{"id":"https://openalex.org/S117679522","display_name":"International Journal of Applied Mathematics and Computer Science","issn_l":"1641-876X","issn":["1641-876X","2083-8492"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320322","host_organization_name":"De Gruyter Open","host_organization_lineage":["https://openalex.org/P4310320322","https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter Open","De Gruyter"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Applied Mathematics and Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G8689627907","display_name":"A new Ability Evaluation Method using the Stress-strength Model and its Implementation to e-learning Systems","funder_award_id":"23650543","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2072132808.pdf","grobid_xml":"https://content.openalex.org/works/W2072132808.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W810437302","https://openalex.org/W1531938792","https://openalex.org/W1554944419","https://openalex.org/W1560724230","https://openalex.org/W1605276600","https://openalex.org/W1964357740","https://openalex.org/W1992223048","https://openalex.org/W1992318318","https://openalex.org/W1996691629","https://openalex.org/W1997425586","https://openalex.org/W2002888413","https://openalex.org/W2009203913","https://openalex.org/W2014828134","https://openalex.org/W2018519044","https://openalex.org/W2024880569","https://openalex.org/W2029732391","https://openalex.org/W2033904036","https://openalex.org/W2037460094","https://openalex.org/W2040704490","https://openalex.org/W2041099987","https://openalex.org/W2045312604","https://openalex.org/W2050063558","https://openalex.org/W2055336003","https://openalex.org/W2059951001","https://openalex.org/W2063978378","https://openalex.org/W2080819414","https://openalex.org/W2082484980","https://openalex.org/W2093873787","https://openalex.org/W2095673361","https://openalex.org/W2101156118","https://openalex.org/W2102201073","https://openalex.org/W2104327430","https://openalex.org/W2114824684","https://openalex.org/W2125048244","https://openalex.org/W2131047005","https://openalex.org/W2135570582","https://openalex.org/W2138964759","https://openalex.org/W2158994553","https://openalex.org/W2162505666","https://openalex.org/W2171033594","https://openalex.org/W2796381354","https://openalex.org/W2811189031","https://openalex.org/W2896916590","https://openalex.org/W2898519191","https://openalex.org/W3125696988","https://openalex.org/W3144619878","https://openalex.org/W3149470707","https://openalex.org/W4235037173","https://openalex.org/W4236856801","https://openalex.org/W4256386524","https://openalex.org/W4291327732","https://openalex.org/W4299837303"],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W3027745756","https://openalex.org/W3205213561","https://openalex.org/W2531880140","https://openalex.org/W2036609560","https://openalex.org/W346861917","https://openalex.org/W3024018414","https://openalex.org/W2126145365","https://openalex.org/W4380081032","https://openalex.org/W1542592062"],"abstract_inverted_index":{"In":[0,127,148],"the":[1,24,64,67,85,93,110,128,132,139,149,152,162,170,177,183,187],"present":[2],"article,":[3],"an":[4,17,59],"attempt":[5],"is":[6,32,55,74,106,180],"made":[7,56],"to":[8,57,108],"derive":[9],"optimal":[10,60],"data-driven":[11],"machine":[12],"learning":[13],"methods":[14,41,105,141],"for":[15],"forecasting":[16,175],"average":[18,52,122],"daily":[19,111],"and":[20,42,51,76,96,112,123,161],"monthly":[21,113],"rainfall":[22],"of":[23,66,103,131,138,176,186],"Fukuoka":[25],"city":[26],"in":[27],"Japan.":[28],"This":[29],"comparative":[30],"study":[31],"conducted":[33],"concentrating":[34],"on":[35,169],"three":[36],"aspects:":[37],"modelling":[38,40,65],"inputs,":[39],"pre-processing":[43,118],"techniques.":[44],"A":[45],"comparison":[46],"between":[47],"linear":[48],"correlation":[49],"analysis":[50],"mutual":[53],"information":[54],"find":[58],"input":[61],"technique.":[62],"For":[63],"rainfall,":[68,114],"a":[69,117,157],"novel":[70],"hybrid":[71,133,178],"multi-model":[72],"method":[73],"proposed":[75],"compared":[77],"with":[78,116,144,156],"its":[79],"constituent":[80],"models.":[81,190],"The":[82,174],"models":[83,165],"include":[84],"artificial":[86],"neural":[87],"network,":[88],"multivariate":[89],"adaptive":[90],"regression":[91],"splines,":[92],"k-nearest":[94],"neighbour,":[95],"radial":[97],"basis":[98],"support":[99],"vector":[100],"regression.":[101],"Each":[102],"these":[104],"applied":[107],"model":[109,179],"coupled":[115],"technique":[119,160],"including":[120],"moving":[121],"principal":[124],"component":[125],"analysis.":[126],"first":[129],"stage":[130],"method,":[134],"sub-models":[135,153],"from":[136],"each":[137],"above":[140],"are":[142,154,166],"constructed":[143],"different":[145],"parameter":[146],"settings.":[147],"second":[150],"stage,":[151],"ranked":[155,164],"variable":[158],"selection":[159],"higher":[163],"selected":[167,189],"based":[168],"leave-one-out":[171],"cross-validation":[172],"error.":[173],"performed":[181],"by":[182],"weighted":[184],"combination":[185],"finally":[188]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":2}],"updated_date":"2026-08-29T07:29:34.045763","created_date":"2025-10-10T00:00:00"}