{"id":"https://openalex.org/W4391489653","doi":"https://doi.org/10.1145/3603273.3630507","title":"Deep Learning Models in Climate Forecasting: Algorithms, Uncertainties and Benchmark Comparison","display_name":"Deep Learning Models in Climate Forecasting: Algorithms, Uncertainties and Benchmark Comparison","publication_year":2023,"publication_date":"2023-11-18","ids":{"openalex":"https://openalex.org/W4391489653","doi":"https://doi.org/10.1145/3603273.3630507"},"language":"en","primary_location":{"id":"doi:10.1145/3603273.3630507","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3603273.3630507","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications","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":null,"display_name":"Ting-Yong Liu","orcid":"https://orcid.org/0009-0000-4321-4437"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ting-Yong Liu","raw_affiliation_strings":["Homestead High School, USA"],"raw_orcid":"https://orcid.org/0009-0000-4321-4437","affiliations":[{"raw_affiliation_string":"Homestead High School, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049467785","display_name":"Yong Yang","orcid":"https://orcid.org/0000-0003-1710-161X"},"institutions":[{"id":"https://openalex.org/I4210122543","display_name":"Guangdong Polytechnic Normal University","ror":"https://ror.org/02pcb5m77","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Yang","raw_affiliation_strings":["School of Mechatronics Engineering, Guangdong Polytechnic Normal University, China"],"raw_orcid":"https://orcid.org/0000-0003-1710-161X","affiliations":[{"raw_affiliation_string":"School of Mechatronics Engineering, Guangdong Polytechnic Normal University, China","institution_ids":["https://openalex.org/I4210122543"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Fu Huang","orcid":"https://orcid.org/0009-0002-0092-4222"},"institutions":[{"id":"https://openalex.org/I4210122543","display_name":"Guangdong Polytechnic Normal University","ror":"https://ror.org/02pcb5m77","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fu Huang","raw_affiliation_strings":["School of Mechatronics Engineering, Guangdong Polytechnic Normal University, China"],"raw_orcid":"https://orcid.org/0009-0002-0092-4222","affiliations":[{"raw_affiliation_string":"School of Mechatronics Engineering, Guangdong Polytechnic Normal University, China","institution_ids":["https://openalex.org/I4210122543"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101881083","display_name":"Guanfeng Liu","orcid":"https://orcid.org/0000-0002-5419-3229"},"institutions":[{"id":"https://openalex.org/I4210122543","display_name":"Guangdong Polytechnic Normal University","ror":"https://ror.org/02pcb5m77","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210122543"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanfeng Liu","raw_affiliation_strings":["School of Mechatronics Engineering, Guangdong Polytechnic Normal University, China"],"raw_orcid":"https://orcid.org/0000-0002-5419-3229","affiliations":[{"raw_affiliation_string":"School of Mechatronics Engineering, Guangdong Polytechnic Normal University, China","institution_ids":["https://openalex.org/I4210122543"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1567,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53837487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"49","last_page":"53"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.9995999932289124,"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9969000220298767,"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/overfitting","display_name":"Overfitting","score":0.7279587984085083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6565576195716858},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6563175916671753},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6468254327774048},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6447630524635315},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6323907971382141},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5078497529029846},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37356290221214294},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3682748079299927}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7279587984085083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6565576195716858},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6563175916671753},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6468254327774048},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6447630524635315},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6323907971382141},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5078497529029846},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37356290221214294},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3682748079299927},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3603273.3630507","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3603273.3630507","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 International Conference on Advances in Artificial Intelligence and Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.8399999737739563,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1971333590","https://openalex.org/W2193503481","https://openalex.org/W2809789958","https://openalex.org/W2937082058","https://openalex.org/W3025949386","https://openalex.org/W3177525997","https://openalex.org/W6702248584"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W3099765033","https://openalex.org/W2989932438","https://openalex.org/W4387297750","https://openalex.org/W2186333919","https://openalex.org/W4380075502"],"abstract_inverted_index":{"This":[0],"paper":[1],"aims":[2],"to":[3,70,84,92],"analyze":[4],"and":[5,52,107,121,129],"compare":[6,104],"the":[7,22,27,33,36,43,49,72,86,105,116,127],"performances":[8],"of":[9,29,48,88,110,131],"several":[10],"representative":[11],"machine":[12],"(deep)":[13],"learning":[14,135],"algorithms":[15,90],"in":[16,35,114,142],"global":[17,75,143],"climate":[18,144],"forecasting.":[19,77,145],"We":[20,78],"choose":[21],"benchmark":[23],"problem":[24],"that":[25,103],"predicts":[26],"distribution":[28],"average":[30,117],"temperatures":[31,51],"along":[32],"globe":[34],"near":[37],"future":[38],"given":[39],"historical":[40],"records.":[41],"Notice":[42],"similarity":[44],"between":[45,119],"a":[46,53],"graph":[47],"spatial":[50],"2D":[54],"image,":[55],"many":[56],"deep":[57,134],"neural":[58],"network":[59],"models,":[60],"originally":[61],"targeted":[62],"for":[63,74],"image":[64],"classification,":[65],"can":[66],"now":[67],"be":[68],"adopted":[69],"learn":[71],"models":[73,113],"temperature":[76,118],"also":[79],"examine":[80],"batch":[81],"optimization":[82],"techniques":[83],"reduce":[85],"uncertainties":[87],"these":[89,111],"due":[91],"their":[93],"inherent":[94],"drawbacks":[95],"such":[96],"as":[97],"overfitting.":[98],"Experiments":[99],"are":[100],"carried":[101],"out":[102],"accuracies":[106],"mean":[108],"bias":[109],"three":[112],"predicting":[115],"2015":[120],"2017.":[122],"These":[123],"experimental":[124],"results":[125],"demonstrate":[126],"advantages":[128],"disadvantages":[130],"each":[132],"individual":[133],"model,":[136],"providing":[137],"insights":[138],"into":[139],"further":[140],"research":[141]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}