{"id":"https://openalex.org/W4404030598","doi":"https://doi.org/10.1109/icccnt61001.2024.10724116","title":"Cloud And Rain Streak Image Analysis For Rainfall Prediction Using Deep Learning Approaches","display_name":"Cloud And Rain Streak Image Analysis For Rainfall Prediction Using Deep Learning Approaches","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4404030598","doi":"https://doi.org/10.1109/icccnt61001.2024.10724116"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt61001.2024.10724116","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt61001.2024.10724116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5089502126","display_name":"E. Gothai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"E Gothai","raw_affiliation_strings":["Kongu Engineering College,Department of CT-PG,Erode,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kongu Engineering College,Department of CT-PG,Erode,India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066687571","display_name":"N. Sasipriyaa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"N. Sasipriyaa","raw_affiliation_strings":["Kongu Engineering College,Department of CSE,Erode,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kongu Engineering College,Department of CSE,Erode,India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111015337","display_name":"P. Natesan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P. Natesan","raw_affiliation_strings":["Kongu Engineering College,Department of It,Erode,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kongu Engineering College,Department of It,Erode,India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067480068","display_name":"S. Kavinkumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. Kavinkumar","raw_affiliation_strings":["M.Sc Software Systems Kongu Engineering College,Erode,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"M.Sc Software Systems Kongu Engineering College,Erode,India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9521999955177307,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9521999955177307,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9430000185966492,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9352999925613403,"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/streak","display_name":"Streak","score":0.886208176612854},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.6901501417160034},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6041019558906555},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.48724284768104553},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47513678669929504},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4586105942726135},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.40813368558883667},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.39461350440979004},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.30193865299224854},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.0844426155090332},{"id":"https://openalex.org/keywords/geophysics","display_name":"Geophysics","score":0.07774618268013}],"concepts":[{"id":"https://openalex.org/C65185188","wikidata":"https://www.wikidata.org/wiki/Q107775","display_name":"Streak","level":2,"score":0.886208176612854},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.6901501417160034},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6041019558906555},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48724284768104553},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47513678669929504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4586105942726135},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.40813368558883667},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.39461350440979004},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.30193865299224854},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0844426155090332},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.07774618268013},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt61001.2024.10724116","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt61001.2024.10724116","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8799999952316284,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2917575296","https://openalex.org/W3005026764","https://openalex.org/W3011867832","https://openalex.org/W3047021033","https://openalex.org/W3096798174","https://openalex.org/W3129908168","https://openalex.org/W3206213776","https://openalex.org/W3211539727","https://openalex.org/W4210629699","https://openalex.org/W4281700753","https://openalex.org/W4281746913","https://openalex.org/W4283520331","https://openalex.org/W4308540792","https://openalex.org/W4309971527","https://openalex.org/W4310792301","https://openalex.org/W4312744028","https://openalex.org/W4317437760","https://openalex.org/W4362496485","https://openalex.org/W4384197320","https://openalex.org/W4385251033","https://openalex.org/W6685352114","https://openalex.org/W6745560452","https://openalex.org/W6746638498"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2082818786","https://openalex.org/W2000740899","https://openalex.org/W2371834895","https://openalex.org/W2997637732","https://openalex.org/W2381571063","https://openalex.org/W2090450991","https://openalex.org/W213647845","https://openalex.org/W2047678803"],"abstract_inverted_index":{"Rainfall":[0],"prediction":[1,149,241],"plays":[2],"a":[3,65,90],"pivotal":[4],"role":[5],"in":[6,134],"weather":[7],"forecasting":[8],"and":[9,23,40,49,63,97,128,159,163,179,184,221,257],"environmental":[10],"management.":[11],"It":[12],"has":[13,45],"significant":[14],"implications":[15],"for":[16,33,110,119,147,239,260],"agriculture,":[17],"water":[18],"resource":[19],"management,":[20],"disaster":[21],"preparedness,":[22],"many":[24],"other":[25,258],"sectors.":[26],"The":[27,53,137,180,188],"use":[28],"of":[29,58,76,93,106,142,195,207,219,227,255],"deep":[30,112,144,166],"learning":[31,113,145,167],"techniques":[32],"predicting":[34],"rainfall":[35,61,77,122,148,169,240],"based":[36,81],"on":[37,82],"cloud":[38,135,151,243],"image":[39,43,100,152,244],"rain":[41],"streak":[42],"analysis":[44],"been":[46],"gaining":[47],"more":[48,50],"importance":[51,57],"lately.":[52],"study":[54,138],"highlights":[55],"the":[56,74,104,107,140,165,192,213,235,253],"having":[59],"accurate":[60,121],"forecasts":[62],"introduces":[64],"new":[66],"data":[67,170],"preprocessing":[68,116],"methodology":[69],"that":[70,232],"aims":[71],"to":[72,89,103,251],"improve":[73,252],"precision":[75],"predictions.":[78],"Day/Night":[79],"discrimination":[80],"HSV":[83],"(Hue,":[84],"Saturation,":[85],"Value),":[86],"resizing":[87],"images":[88],"standardized":[91],"dimension":[92],"$128":[94],"\\times":[95],"128$,":[96],"introducing":[98],"random":[99],"rotations":[101],"contribute":[102],"robustness":[105],"dataset":[108],"used":[109],"training":[111],"models.":[114],"Effective":[115],"is":[117,234,249],"essential":[118],"enabling":[120],"predictions":[123],"by":[124,198,212],"helping":[125],"models":[126],"learn":[127],"generalize":[129],"effectively":[130],"from":[131,171],"visual":[132],"cues":[133],"images.":[136],"evaluated":[139],"performance":[141],"five":[143],"architectures":[146,259],"using":[150,242],"analysis:":[153],"CNN,":[154],"RNN":[155,222],"(LSTM),":[156],"DNN,":[157],"DenseNet-169,":[158],"ResNet-50.":[160],"To":[161],"train":[162],"test":[164],"models,":[168],"four":[172],"publicly":[173],"available":[174],"datasets,":[175],"namely":[176],"Hindustan":[177],"Times":[178],"Weather":[181],"Channel,":[182],"Swimcat,":[183],"Unsplash,":[185],"was":[186],"used.":[187],"DenseNet-169":[189,233],"architecture":[190,203,215,223,238],"achieved":[191],"best":[193],"accuracy":[194,206,218,226,254],"98.5%,":[196],"followed":[197,211],"CNN":[199],"at":[200],"94.5%.":[201],"DNN":[202],"had":[204],"an":[205,217,225],"$\\mathbf{8":[208],"4":[209],"\\%}$,":[210],"ResNet-50":[214],"with":[216,224],"77%,":[220],"75%.":[228],"These":[229],"results":[230],"suggest":[231],"most":[236],"promising":[237],"analysis.":[245],"However,":[246],"further":[247],"research":[248],"needed":[250],"RNNs":[256],"this":[261],"task.":[262]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
