{"id":"https://openalex.org/W4415125881","doi":"https://doi.org/10.1109/fuzz62266.2025.11197687","title":"Hybridizing Fuzzy Clustering and Ensemble Learning for Accurate River Water Level Prediction","display_name":"Hybridizing Fuzzy Clustering and Ensemble Learning for Accurate River Water Level Prediction","publication_year":2025,"publication_date":"2025-07-06","ids":{"openalex":"https://openalex.org/W4415125881","doi":"https://doi.org/10.1109/fuzz62266.2025.11197687"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz62266.2025.11197687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11197687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","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/A5083310775","display_name":"Namal Rathnayake","orcid":"https://orcid.org/0000-0002-5235-8552"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Namal Rathnayake","raw_affiliation_strings":["The University of Tokyo,River & Environmental Engineering Laboratory,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,River & Environmental Engineering Laboratory,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027658472","display_name":"Yukinobu Hoshino","orcid":"https://orcid.org/0000-0001-5285-8007"},"institutions":[{"id":"https://openalex.org/I35568498","display_name":"Kochi University of Technology","ror":"https://ror.org/00rghrr56","country_code":"JP","type":"education","lineage":["https://openalex.org/I35568498"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yukinobu Hoshino","raw_affiliation_strings":["School of Systems Engineering, Kochi University of Technology,Kochi,Japan"],"affiliations":[{"raw_affiliation_string":"School of Systems Engineering, Kochi University of Technology,Kochi,Japan","institution_ids":["https://openalex.org/I35568498"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083310775"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27979591,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9959999918937683,"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":0.9959999918937683,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9534000158309937,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9517999887466431,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"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/flex","display_name":"FLEX","score":0.7096999883651733},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.605400025844574},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5785999894142151},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5576000213623047},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4848000109195709},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.42480000853538513},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.41659998893737793},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.39719998836517334}],"concepts":[{"id":"https://openalex.org/C2776252893","wikidata":"https://www.wikidata.org/wiki/Q1364836","display_name":"FLEX","level":2,"score":0.7096999883651733},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6198999881744385},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.605400025844574},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6039000153541565},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5785999894142151},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5576000213623047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5486000180244446},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.546500027179718},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4848000109195709},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.42480000853538513},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.41659998893737793},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C153823671","wikidata":"https://www.wikidata.org/wiki/Q1049799","display_name":"Water resources","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3677999973297119},{"id":"https://openalex.org/C1284942","wikidata":"https://www.wikidata.org/wiki/Q1929501","display_name":"Water level","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2840999960899353},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.2605000138282776}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz62266.2025.11197687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11197687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W3015873955","https://openalex.org/W3118336383","https://openalex.org/W3136837099","https://openalex.org/W3153515209","https://openalex.org/W3206589882","https://openalex.org/W4210386847","https://openalex.org/W4226025382","https://openalex.org/W4283811973","https://openalex.org/W4285594059","https://openalex.org/W4285595089","https://openalex.org/W4308871212","https://openalex.org/W4317815257","https://openalex.org/W4367047585","https://openalex.org/W4384826028","https://openalex.org/W4385782242","https://openalex.org/W4391015930","https://openalex.org/W4401956925"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"river":[1],"water":[2,11],"level":[3],"prediction":[4],"is":[5],"crucial":[6],"for":[7,88,95],"disaster":[8],"management":[9],"and":[10,91,101],"resource":[12],"planning.":[13],"This":[14],"study":[15],"introduces":[16],"Flex":[17,43,82],"Bag,":[18],"a":[19,55,85,93],"hybrid":[20],"framework":[21],"integrating":[22],"fuzzy":[23],"c-means":[24],"clustering":[25],"with":[26,37],"machine":[27],"learning":[28],"models.":[29],"By":[30],"combining":[31],"global":[32],"relationships":[33],"from":[34,40],"base":[35],"models":[36],"localized":[38],"insights":[39],"FCM":[41],"features,":[42],"Bag":[44,83],"enhances":[45],"predictive":[46],"accuracy.":[47],"Experiments":[48],"on":[49],"the":[50,75],"Niyodo":[51],"River":[52],"dataset":[53],"show":[54],"reduced":[56],"root":[57],"mean":[58],"square":[59],"error":[60],"(34.09)":[61],"compared":[62],"to":[63],"Cascaded-ANFIS":[64],"(35.51),":[65],"highlighting":[66],"its":[67],"robustness":[68],"in":[69,78],"rainfall-runoff":[70],"modeling.":[71],"Visual":[72],"analyses":[73],"validate":[74],"framework's":[76],"effectiveness":[77],"capturing":[79],"spatial-temporal":[80],"patterns.":[81],"offers":[84],"scalable":[86],"solution":[87],"hydrological":[89],"challenges":[90],"sets":[92],"foundation":[94],"handling":[96],"real-world":[97],"issues":[98],"like":[99],"missing":[100],"noisy":[102],"data.":[103]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-14T00:00:00"}