{"id":"https://openalex.org/W4387250552","doi":"https://doi.org/10.1109/icicat57735.2023.10263728","title":"Artificial Intelligence Based Heatwave Intensity Prediction Model","display_name":"Artificial Intelligence Based Heatwave Intensity Prediction Model","publication_year":2023,"publication_date":"2023-06-23","ids":{"openalex":"https://openalex.org/W4387250552","doi":"https://doi.org/10.1109/icicat57735.2023.10263728"},"language":"en","primary_location":{"id":"doi:10.1109/icicat57735.2023.10263728","doi":"https://doi.org/10.1109/icicat57735.2023.10263728","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icicat57735.2023.10263728","pdf_url":null,"source":{"id":"https://openalex.org/S7407087315","display_name":"International Conference on IoT, Communication and Automation Technology (ICICAT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on IoT, Communication and Automation Technology (ICICAT)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5027451294","display_name":"Aniruddha Mane","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210146358","display_name":"Raisoni Group of Institutions","ror":"https://ror.org/03dp11s58","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146358"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Aniruddha Mane","raw_affiliation_strings":["G H Raisoni College of Engineering,Data Science (Center of Excellence),Nagpur,India","Data Science (Center of Excellence), G H Raisoni College of Engineering, Nagpur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"G H Raisoni College of Engineering,Data Science (Center of Excellence),Nagpur,India","institution_ids":["https://openalex.org/I4210146358"]},{"raw_affiliation_string":"Data Science (Center of Excellence), G H Raisoni College of Engineering, Nagpur, India","institution_ids":["https://openalex.org/I4210146358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092984925","display_name":"Nakul Lekurwale","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210146358","display_name":"Raisoni Group of Institutions","ror":"https://ror.org/03dp11s58","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146358"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nakul Lekurwale","raw_affiliation_strings":["G H Raisoni College of Engineering,Data Science (Center of Excellence),Nagpur,India","Data Science (Center of Excellence), G H Raisoni College of Engineering, Nagpur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"G H Raisoni College of Engineering,Data Science (Center of Excellence),Nagpur,India","institution_ids":["https://openalex.org/I4210146358"]},{"raw_affiliation_string":"Data Science (Center of Excellence), G H Raisoni College of Engineering, Nagpur, India","institution_ids":["https://openalex.org/I4210146358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107743733","display_name":"Priya B. Dasarwar","orcid":"https://orcid.org/0009-0007-9551-7676","observed_orcids":["https://orcid.org/0009-0007-9551-7676"]},"institutions":[{"id":"https://openalex.org/I4210146358","display_name":"Raisoni Group of Institutions","ror":"https://ror.org/03dp11s58","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146358"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Priya Maidamwar","raw_affiliation_strings":["G H Raisoni College of Engineering, Nagpur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"G H Raisoni College of Engineering, Nagpur, India","institution_ids":["https://openalex.org/I4210146358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063588747","display_name":"Prashant Khobragade","orcid":"https://orcid.org/0000-0002-9646-0604","observed_orcids":["https://orcid.org/0000-0002-9646-0604"]},"institutions":[{"id":"https://openalex.org/I4210146358","display_name":"Raisoni Group of Institutions","ror":"https://ror.org/03dp11s58","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146358"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prashant Khobragade","raw_affiliation_strings":["G H Raisoni College of Engineering, Nagpur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"G H Raisoni College of Engineering, Nagpur, India","institution_ids":["https://openalex.org/I4210146358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025010142","display_name":"Snehlata Dongre","orcid":"https://orcid.org/0000-0002-1080-2109","observed_orcids":["https://orcid.org/0000-0002-1080-2109"]},"institutions":[{"id":"https://openalex.org/I4210146358","display_name":"Raisoni Group of Institutions","ror":"https://ror.org/03dp11s58","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146358"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Snehlata Dongre","raw_affiliation_strings":["G H Raisoni College of Engineering, Nagpur, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"G H Raisoni College of Engineering, Nagpur, India","institution_ids":["https://openalex.org/I4210146358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210146358"],"apc_list":null,"apc_paid":null,"fwci":10.8465,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.99247036,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.16529999673366547,"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":0.16529999673366547,"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/T10029","display_name":"Climate variability and models","score":0.15309999883174896,"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/T11244","display_name":"Climate Change and Health Impacts","score":0.10379999876022339,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"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/heatwave-severity","display_name":"heatwave severity","score":0.6100000143051147},{"id":"https://openalex.org/keywords/machine-learning-regression","display_name":"machine learning regression","score":0.7620000243186951},{"id":"https://openalex.org/keywords/historical-temperature-data","display_name":"historical temperature data","score":0.9520000219345093},{"id":"https://openalex.org/keywords/heat-risk-management","display_name":"heat risk management","score":0.6809999942779541}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7860468029975891},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6981937885284424},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6469700932502747},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5170345902442932},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.47944632172584534},{"id":"https://openalex.org/C93038891","wikidata":"https://www.wikidata.org/wiki/Q1061524","display_name":"Intensity (physics)","level":2,"score":0.4653438329696655},{"id":"https://openalex.org/C2993220731","wikidata":"https://www.wikidata.org/wiki/Q215864","display_name":"Heat wave","level":3,"score":0.4635331928730011},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.4270968437194824},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1884361207485199},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.11621057987213135},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1003759503364563},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.089633047580719},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icicat57735.2023.10263728","doi":"https://doi.org/10.1109/icicat57735.2023.10263728","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icicat57735.2023.10263728","pdf_url":null,"source":{"id":"https://openalex.org/S7407087315","display_name":"International Conference on IoT, Communication and Automation Technology (ICICAT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Conference on IoT, Communication and Automation Technology (ICICAT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5378999710083008,"id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2927286370","https://openalex.org/W2951618319","https://openalex.org/W3000513984","https://openalex.org/W3108635881","https://openalex.org/W3119802920","https://openalex.org/W3137094912","https://openalex.org/W3176547784","https://openalex.org/W3193888432","https://openalex.org/W3204382304","https://openalex.org/W4210477899","https://openalex.org/W4289515433","https://openalex.org/W4311237194","https://openalex.org/W4362627411"],"related_works":["https://openalex.org/W4411851800","https://openalex.org/W4417404199","https://openalex.org/W4283120639","https://openalex.org/W4414464492","https://openalex.org/W4411484068","https://openalex.org/W4408400248","https://openalex.org/W7131355458","https://openalex.org/W4387715599","https://openalex.org/W7131654567","https://openalex.org/W4408429780"],"abstract_inverted_index":{"Telangana":[0],"experiences":[1],"heat":[2,37,128],"waves":[3,16],"frequently":[4],"and":[5,25,66,83,104,107,113,151,162],"is":[6],"more":[7,102],"susceptible":[8],"to":[9,63,89],"various":[10],"sorts":[11],"of":[12,34,94,155],"high":[13,81],"heat.":[14],"Heat":[15],"have":[17],"a":[18,101,143],"significant":[19],"impact":[20],"on":[21,57,122],"infrastructure,":[22],"the":[23,69,92,118,123,153],"environment,":[24],"human":[26],"health.":[27],"Effective":[28],"risk":[29,163],"management":[30],"requires":[31],"accurate":[32],"forecasting":[33],"these":[35],"intense":[36],"episodes.":[38],"In":[39,87,115],"this":[40,116,149],"study,":[41],"machine":[42,77,95,136,157],"learning":[43,52,78,96,137,158],"methods":[44],"are":[45],"investigated":[46],"for":[47,111,126,145],"predicting":[48,127],"heatwave":[49,98,160],"severity.":[50],"Machine":[51],"algorithms":[53],"will":[54],"be":[55,73],"trained":[56],"historical":[58],"temperature":[59],"data":[60,124],"in":[61,97,148,159],"order":[62],"discover":[64],"patterns":[65],"connections":[67],"between":[68],"data.":[70],"Heatwave":[71],"may":[72],"effectively":[74],"predicted":[75],"by":[76,133],"models,":[79],"with":[80],"accuracy":[82],"low":[84],"error":[85],"rates.":[86],"comparison":[88],"conventional":[90],"methods,":[91],"use":[93],"prediction":[99,161],"offers":[100],"effective":[103],"dependable":[105],"solution,":[106],"has":[108],"substantial":[109],"advantages":[110],"communities":[112],"decision-makers.":[114],"experiment,":[117],"highest":[119],"performing":[120],"model":[121],"available":[125],"wave":[129],"intensity":[130],"was":[131],"found":[132],"using":[134],"five":[135],"regression":[138],"models.":[139],"This":[140],"paper":[141],"provides":[142],"foundation":[144],"further":[146],"research":[147],"area":[150],"emphasizes":[152],"significance":[154],"applying":[156],"management.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2023-10-03T00:00:00"}