{"id":"https://openalex.org/W3212955604","doi":"https://doi.org/10.1109/icccnt51525.2021.9579628","title":"A Comparative Study of LGBM-SVR Hybrid Machine Learning Model for Rainfall Prediction","display_name":"A Comparative Study of LGBM-SVR Hybrid Machine Learning Model for Rainfall Prediction","publication_year":2021,"publication_date":"2021-07-06","ids":{"openalex":"https://openalex.org/W3212955604","doi":"https://doi.org/10.1109/icccnt51525.2021.9579628","mag":"3212955604"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt51525.2021.9579628","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt51525.2021.9579628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5082154897","display_name":"Merin Benny Maliyeckel","orcid":null},"institutions":[{"id":"https://openalex.org/I48018076","display_name":"Christ University","ror":"https://ror.org/022tv9y30","country_code":"IN","type":"education","lineage":["https://openalex.org/I48018076"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Merin Benny Maliyeckel","raw_affiliation_strings":["School of Engineering and Technology, Christ, Deemed to be University, Bengaluru"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Technology, Christ, Deemed to be University, Bengaluru","institution_ids":["https://openalex.org/I48018076"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010876794","display_name":"B. Chaithanya Sai","orcid":null},"institutions":[{"id":"https://openalex.org/I48018076","display_name":"Christ University","ror":"https://ror.org/022tv9y30","country_code":"IN","type":"education","lineage":["https://openalex.org/I48018076"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"B. Chaithanya Sai","raw_affiliation_strings":["School of Engineering and Technology, Christ, Deemed to be University, Bengaluru"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Technology, Christ, Deemed to be University, Bengaluru","institution_ids":["https://openalex.org/I48018076"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026571741","display_name":"J. Naveen","orcid":null},"institutions":[{"id":"https://openalex.org/I48018076","display_name":"Christ University","ror":"https://ror.org/022tv9y30","country_code":"IN","type":"education","lineage":["https://openalex.org/I48018076"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"J Naveen","raw_affiliation_strings":["School of Engineering and Technology, Christ, Deemed to be University, Bengaluru"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Technology, Christ, Deemed to be University, Bengaluru","institution_ids":["https://openalex.org/I48018076"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082154897"],"corresponding_institution_ids":["https://openalex.org/I48018076"],"apc_list":null,"apc_paid":null,"fwci":1.0457,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.78712763,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9980000257492065,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9980000257492065,"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"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9975000023841858,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9962000250816345,"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":0.6494103670120239},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5880877375602722},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5776979923248291},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5547102689743042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5311651825904846},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5133377909660339},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4960249364376068},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.43749210238456726},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25217288732528687},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23349961638450623}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6494103670120239},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5880877375602722},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5776979923248291},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5547102689743042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5311651825904846},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5133377909660339},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4960249364376068},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.43749210238456726},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25217288732528687},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23349961638450623},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt51525.2021.9579628","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt51525.2021.9579628","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W740415","https://openalex.org/W1124582155","https://openalex.org/W1548683814","https://openalex.org/W1641696774","https://openalex.org/W2016210396","https://openalex.org/W2039240409","https://openalex.org/W2088211516","https://openalex.org/W2123585936","https://openalex.org/W2160849785","https://openalex.org/W2276751812","https://openalex.org/W2287279778","https://openalex.org/W2290960045","https://openalex.org/W2291811783","https://openalex.org/W2293337803","https://openalex.org/W2298248304","https://openalex.org/W2317974896","https://openalex.org/W2419406374","https://openalex.org/W2570501755","https://openalex.org/W2587853473","https://openalex.org/W2589077383","https://openalex.org/W2597172628","https://openalex.org/W2610314771","https://openalex.org/W2615493097","https://openalex.org/W2718347282","https://openalex.org/W2755158986","https://openalex.org/W2768348081","https://openalex.org/W2988198700","https://openalex.org/W3006330610","https://openalex.org/W3018670396","https://openalex.org/W3111207745","https://openalex.org/W3155828914","https://openalex.org/W4206686222","https://openalex.org/W4213096653","https://openalex.org/W6697120286","https://openalex.org/W6737100851","https://openalex.org/W6745609711"],"related_works":["https://openalex.org/W2090763504","https://openalex.org/W2357256365","https://openalex.org/W148178222","https://openalex.org/W2348502264","https://openalex.org/W2102148524","https://openalex.org/W2104657898","https://openalex.org/W2365486383","https://openalex.org/W2362059367","https://openalex.org/W1948992892","https://openalex.org/W1886884218"],"abstract_inverted_index":{"Weather":[0],"forecasting":[1],"is":[2,22,41,107,120],"a":[3,23,54,74,99,151],"critical":[4],"factor":[5],"in":[6,27],"determining":[7,25],"the":[8,28,80,84,92,105,114,117,132,144],"crop":[9],"production":[10],"and":[11,30,36,50,52,59,71,77,113,139,142],"harvest":[12],"of":[13,32,38,83,94,104,155],"any":[14],"geographical":[15],"location.":[16],"Among":[17],"various":[18,44,111],"other":[19],"factors,":[20],"rainfall":[21],"crucial":[24],"component":[26],"sowing":[29],"harvesting":[31],"crops.":[33],"The":[34,65,102,128],"aim":[35],"intent":[37],"this":[39],"paper":[40],"to":[42,61],"analyze":[43],"machine":[45],"learning":[46],"algorithms":[47],"like":[48],"LightGBM":[49,58,70,138],"SVR,":[51],"develop":[53],"hybrid":[55,66,133],"model":[56,67,89,106,134],"using":[57],"SVR":[60,72,140],"accurately":[62],"predict":[63],"rainfall.":[64,156],"implements":[68],"both":[69],"on":[73,98],"preprocessed":[75],"dataset":[76],"then":[78],"combines":[79],"predicted":[81],"values":[82,96],"results":[85],"through":[86],"an":[87],"ensemble":[88],"which":[90],"considers":[91],"average":[93],"these":[95],"based":[97],"predefined":[100],"weight.":[101],"weight":[103],"determined":[108],"by":[109],"considering":[110],"combinations,":[112],"result":[115],"with":[116],"least":[118,145],"error":[119,149],"taken":[121],"into":[122],"consideration":[123],"for":[124],"that":[125,131],"particular":[126],"dataset.":[127],"study":[129],"shows":[130],"performed":[135],"better":[136],"than":[137],"individually,":[141],"produced":[143],"root":[146],"mean":[147],"square":[148],"yielding":[150],"more":[152],"accurate":[153],"prediction":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
