{"id":"https://openalex.org/W2588374640","doi":"https://doi.org/10.1109/ssci.2016.7849935","title":"A genetic algorithm based feature selection approach for rainfall forecasting in sugarcane areas","display_name":"A genetic algorithm based feature selection approach for rainfall forecasting in sugarcane areas","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2588374640","doi":"https://doi.org/10.1109/ssci.2016.7849935","mag":"2588374640"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2016.7849935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2016.7849935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/A_genetic_algorithm_based_feature_selection_approach_for_rainfall_forecasting_in_sugarcane_areas/13392479","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082630993","display_name":"Ali Haidar","orcid":"https://orcid.org/0000-0001-5092-949X"},"institutions":[{"id":"https://openalex.org/I74899385","display_name":"Central Queensland University","ror":"https://ror.org/023q4bk22","country_code":"AU","type":"education","lineage":["https://openalex.org/I74899385"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ali Haidar","raw_affiliation_strings":["Center for Intelligent Systems, Central Queensland University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Intelligent Systems, Central Queensland University, Australia","institution_ids":["https://openalex.org/I74899385"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026528338","display_name":"Brijesh Verma","orcid":"https://orcid.org/0000-0002-4618-0479"},"institutions":[{"id":"https://openalex.org/I74899385","display_name":"Central Queensland University","ror":"https://ror.org/023q4bk22","country_code":"AU","type":"education","lineage":["https://openalex.org/I74899385"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Brijesh Verma","raw_affiliation_strings":["Center for Intelligent Systems, Central Queensland University, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Intelligent Systems, Central Queensland University, Australia","institution_ids":["https://openalex.org/I74899385"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74899385"],"apc_list":null,"apc_paid":null,"fwci":2.4054,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.89689018,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":"2016","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9883000254631042,"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"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9883000254631042,"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/T12431","display_name":"Sugarcane Cultivation and Processing","score":0.984499990940094,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9503999948501587,"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/mean-squared-error","display_name":"Mean squared error","score":0.787411093711853},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.5963335037231445},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5487752556800842},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5355128645896912},{"id":"https://openalex.org/keywords/precipitation","display_name":"Precipitation","score":0.5094996094703674},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5055058002471924},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.493464857339859},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4546010196208954},{"id":"https://openalex.org/keywords/profitability-index","display_name":"Profitability index","score":0.4279289245605469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4137096107006073},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.37372738122940063},{"id":"https://openalex.org/keywords/climatology","display_name":"Climatology","score":0.3499235510826111},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.29376840591430664},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.28398650884628296},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22946777939796448},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15689513087272644},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07785823941230774}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.787411093711853},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.5963335037231445},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5487752556800842},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5355128645896912},{"id":"https://openalex.org/C107054158","wikidata":"https://www.wikidata.org/wiki/Q25257","display_name":"Precipitation","level":2,"score":0.5094996094703674},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5055058002471924},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.493464857339859},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4546010196208954},{"id":"https://openalex.org/C129361004","wikidata":"https://www.wikidata.org/wiki/Q2470236","display_name":"Profitability index","level":2,"score":0.4279289245605469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4137096107006073},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.37372738122940063},{"id":"https://openalex.org/C49204034","wikidata":"https://www.wikidata.org/wiki/Q52139","display_name":"Climatology","level":1,"score":0.3499235510826111},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29376840591430664},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28398650884628296},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22946777939796448},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15689513087272644},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07785823941230774},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ssci.2016.7849935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2016.7849935","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"},{"id":"pmh:oai:acquire.cqu.edu.au:cqu:14000","is_oa":false,"landing_page_url":"http://hdl.cqu.edu.au/10018/1213268","pdf_url":null,"source":{"id":"https://openalex.org/S4306400635","display_name":"Acquire (CQUniversity)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I74899385","host_organization_name":"Central Queensland University","host_organization_lineage":["https://openalex.org/I74899385"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Haidar, A, Verma, B, (2016), A genetic algorithm based feature selection approach for rainfall forecasting in sugarcane areas, 06-09 December 2016, IEEE Symposium Series on Computational Intelligence, 2016 (SSCI), Athens, Greece, IEEE, Piscataway, NJ., p. 1-8, http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7840087","raw_type":"conference"},{"id":"pmh:oai:figshare.com:article/13392479","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/A_genetic_algorithm_based_feature_selection_approach_for_rainfall_forecasting_in_sugarcane_areas/13392479","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference contribution"},{"id":"mag:2789428796","is_oa":false,"landing_page_url":"http://jglobal.jst.go.jp/en/public/201702231557238881","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/13392479","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/A_genetic_algorithm_based_feature_selection_approach_for_rainfall_forecasting_in_sugarcane_areas/13392479","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference contribution"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6100000143051147,"display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320980","display_name":"Central Queensland University","ror":"https://ror.org/023q4bk22"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W43382659","https://openalex.org/W52495929","https://openalex.org/W78766501","https://openalex.org/W1773817722","https://openalex.org/W2001593107","https://openalex.org/W2002108249","https://openalex.org/W2003064945","https://openalex.org/W2026123199","https://openalex.org/W2039049978","https://openalex.org/W2042105177","https://openalex.org/W2241087191","https://openalex.org/W6603216745"],"related_works":["https://openalex.org/W3125099825","https://openalex.org/W25115902","https://openalex.org/W2753408573","https://openalex.org/W4285147705","https://openalex.org/W1986338457","https://openalex.org/W2066844180","https://openalex.org/W4293192099","https://openalex.org/W4253553932","https://openalex.org/W3132513730","https://openalex.org/W2984525013"],"abstract_inverted_index":{"Rainfall":[0],"is":[1,95],"a":[2,51,130],"vital":[3],"phenomenon":[4],"that":[5,81,100,108],"contributes":[6],"in":[7,22,72,138,168],"the":[8,18,26,29,83,122,126,146,162],"success":[9],"of":[10,20,28,33,136,143],"sugar":[11],"industry":[12],"season.":[13,30],"The":[14,75,93,153],"ability":[15],"to":[16,41,59,120,158],"determine":[17,60],"amount":[19],"precipitation":[21],"sugarcane":[23,73],"areas":[24],"enhances":[25],"profitability":[27],"Different":[31],"types":[32],"climate":[34,62],"indices":[35,63],"and":[36,64,113,161],"attributes":[37,65],"are":[38,66,79],"usually":[39],"applied":[40],"model":[42,155,164],"rainfall":[43,70,87],"forecasting":[44,71,88,104,169],"systems.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49],"present":[50],"novel":[52],"genetic":[53,147],"algorithm":[54,148],"based":[55,149],"feature":[56],"selection":[57],"approach":[58,94],"which":[61],"most":[67,76],"significant":[68,77],"for":[69,86,145],"areas.":[74],"features":[78,80],"return":[82],"highest":[84],"accuracy":[85,167],"through":[89],"artificial":[90],"neural":[91],"networks.":[92],"evaluated":[96],"on":[97],"real-world":[98],"data":[99],"contain":[101],"different":[102],"weather":[103],"features.":[105],"A":[106],"set":[107],"contains":[109],"maximum":[110],"temperature":[111],"values":[112],"Southern":[114],"Oscillation":[115],"Index":[116],"(SOI)":[117],"has":[118],"proven":[119],"be":[121],"best":[123],"combination":[124],"among":[125],"other":[127,159],"models":[128,160],"with":[129],"Root":[131],"Mean":[132],"Square":[133],"Error":[134],"(RMSE)":[135],"0.027":[137],"November.":[139],"An":[140],"Average":[141],"RMSE":[142],"0.0638":[144],"forecasts":[150],"was":[151,156],"recorded.":[152],"proposed":[154,163],"compared":[157],"revealed":[165],"higher":[166],"monthly":[170],"rainfall.":[171]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":5}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}
