{"id":"https://openalex.org/W3109044333","doi":"https://doi.org/10.1109/ccece47787.2020.9255716","title":"A Deep Learning Approach to Predict Weather Data Using Cascaded LSTM Network","display_name":"A Deep Learning Approach to Predict Weather Data Using Cascaded LSTM Network","publication_year":2020,"publication_date":"2020-08-30","ids":{"openalex":"https://openalex.org/W3109044333","doi":"https://doi.org/10.1109/ccece47787.2020.9255716","mag":"3109044333"},"language":"en","primary_location":{"id":"doi:10.1109/ccece47787.2020.9255716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccece47787.2020.9255716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","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/A5043703631","display_name":"Zarif Al Sadeque","orcid":null},"institutions":[{"id":"https://openalex.org/I32625721","display_name":"University of Saskatchewan","ror":"https://ror.org/010x8gc63","country_code":"CA","type":"education","lineage":["https://openalex.org/I32625721"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zarif Al Sadeque","raw_affiliation_strings":["Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada","institution_ids":["https://openalex.org/I32625721"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071017561","display_name":"Francis M. Bui","orcid":"https://orcid.org/0000-0002-8799-5965"},"institutions":[{"id":"https://openalex.org/I32625721","display_name":"University of Saskatchewan","ror":"https://ror.org/010x8gc63","country_code":"CA","type":"education","lineage":["https://openalex.org/I32625721"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Francis M. Bui","raw_affiliation_strings":["Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, Canada","institution_ids":["https://openalex.org/I32625721"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I32625721"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9983000159263611,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9940999746322632,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7334930300712585},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7129650115966797},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6724075078964233},{"id":"https://openalex.org/keywords/dew-point","display_name":"Dew point","score":0.669523298740387},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6386098861694336},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.545593798160553},{"id":"https://openalex.org/keywords/weather-prediction","display_name":"Weather prediction","score":0.5291260480880737},{"id":"https://openalex.org/keywords/numerical-weather-prediction","display_name":"Numerical weather prediction","score":0.4997258186340332},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4994525909423828},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.4934510290622711},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.46936824917793274},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4678676128387451},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.17716166377067566}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7334930300712585},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7129650115966797},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6724075078964233},{"id":"https://openalex.org/C82210777","wikidata":"https://www.wikidata.org/wiki/Q178828","display_name":"Dew point","level":2,"score":0.669523298740387},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6386098861694336},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.545593798160553},{"id":"https://openalex.org/C2987469573","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather prediction","level":2,"score":0.5291260480880737},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.4997258186340332},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4994525909423828},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.4934510290622711},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.46936824917793274},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4678676128387451},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.17716166377067566},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccece47787.2020.9255716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccece47787.2020.9255716","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1641696774","https://openalex.org/W1964298122","https://openalex.org/W2002521620","https://openalex.org/W2018519044","https://openalex.org/W2084068786","https://openalex.org/W2149320615","https://openalex.org/W2263189687","https://openalex.org/W2521003103","https://openalex.org/W2569758175","https://openalex.org/W2884797809","https://openalex.org/W2893954686","https://openalex.org/W2932104440","https://openalex.org/W2945584124","https://openalex.org/W2954011044","https://openalex.org/W3011388229","https://openalex.org/W3120882540","https://openalex.org/W6788733023"],"related_works":["https://openalex.org/W1563787591","https://openalex.org/W2321020009","https://openalex.org/W3021464272","https://openalex.org/W3081577063","https://openalex.org/W4385699815","https://openalex.org/W2787716671","https://openalex.org/W4320929292","https://openalex.org/W948991213","https://openalex.org/W2921756899","https://openalex.org/W2020612098"],"abstract_inverted_index":{"Weather":[0],"prediction":[1],"is":[2],"a":[3,48,75,79,97,108,138,143],"challenging":[4],"research":[5],"problem":[6,35],"although":[7],"the":[8,16,63,71,103,120,157,163,167,179,186],"revolutionary":[9],"advancement":[10],"in":[11,27,74,84,89,107,129,193],"deep":[12,52],"learning,":[13],"along":[14],"with":[15,78,134],"availability":[17],"of":[18,29,62,82,140,148],"big":[19],"data,":[20],"has":[21,36],"significantly":[22,183],"alleviated":[23],"this":[24,34,130],"problem.":[25],"Moreover,":[26],"terms":[28],"robustness":[30],"and":[31,127,132,170],"computational":[32],"cost,":[33],"currently":[37],"interested":[38],"many":[39],"researchers":[40],"to":[41,101,118],"develop":[42],"numerous":[43],"models.":[44,66],"This":[45,67],"paper":[46],"proposes":[47],"lightweight":[49],"yet":[50],"powerful":[51],"learning":[53,145],"architecture":[54,68],"for":[55,96,166],"weather":[56,91,105,164],"forecasting":[57],"that":[58,178],"can":[59],"outperform":[60],"some":[61],"existing":[64],"well-known":[65],"mainly":[69],"uses":[70],"LSTM":[72,141,149,188],"layers":[73],"stacked":[76],"fashion,":[77],"different":[80,135],"number":[81,139,147],"units":[83],"each":[85],"layer.":[86],"It":[87],"takes":[88],"multiple":[90],"variables":[92],"as":[93],"input":[94],"features":[95],"given":[98],"time":[99],"sequence":[100],"forecast":[102],"same":[104],"parameters":[106,165],"multi-input":[109],"multi-output":[110],"(MIMO)":[111],"structure.":[112],"The":[113,174],"resulting":[114],"models":[115,152,181],"are":[116],"tested":[117],"predict":[119],"wind":[121],"speed,":[122],"relative":[123],"humidity,":[124],"dew":[125],"point":[126],"temperature":[128],"study":[131],"experimented":[133],"hyper-parameters":[136],"consisting":[137],"layers,":[142],"variable":[144],"rate,":[146],"units.":[150],"Two":[151],"have":[153],"been":[154],"built":[155],"cascading":[156],"basic":[158],"1hour-ahead":[159],"model":[160],"which":[161],"predicts":[162],"2":[168],"hours":[169,172],"3":[171],"ahead.":[173],"obtained":[175],"results":[176],"show":[177],"cascaded":[180],"perform":[182],"better":[184],"than":[185],"standard":[187],"or":[189],"1D":[190],"convolution":[191],"networks":[192],"shorter":[194],"period":[195],"prediction.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}