{"id":"https://openalex.org/W4385691571","doi":"https://doi.org/10.1145/3605801.3605841","title":"LSTM-GATs: Wind field prediction based on graph attention deep neural network","display_name":"LSTM-GATs: Wind field prediction based on graph attention deep neural network","publication_year":2023,"publication_date":"2023-06-16","ids":{"openalex":"https://openalex.org/W4385691571","doi":"https://doi.org/10.1145/3605801.3605841"},"language":"en","primary_location":{"id":"doi:10.1145/3605801.3605841","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3605801.3605841","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 2nd International Conference on Networks, Communications and Information Technology","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":"https://openalex.org/A5089812264","display_name":"Shengjie Zhou","orcid":"https://orcid.org/0009-0000-3159-2908"},"institutions":[{"id":"https://openalex.org/I24201400","display_name":"Chengdu University of Information Technology","ror":"https://ror.org/01yxwrh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I24201400"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shengjie Zhou","raw_affiliation_strings":["Chengdu University of Information Technology, China"],"raw_orcid":"https://orcid.org/0009-0000-3159-2908","affiliations":[{"raw_affiliation_string":"Chengdu University of Information Technology, China","institution_ids":["https://openalex.org/I24201400"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5089812264"],"corresponding_institution_ids":["https://openalex.org/I24201400"],"apc_list":null,"apc_paid":null,"fwci":0.1278,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43032165,"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":"208","last_page":"212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9987000226974487,"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":0.9987000226974487,"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9699000120162964,"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9628000259399414,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.680124044418335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.662004828453064},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5472570657730103},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.5433341860771179},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.504824697971344},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.502612829208374},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.47386932373046875},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.4632296562194824},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4433203339576721},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3892270624637604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3673819899559021},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3395084738731384},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14301636815071106}],"concepts":[{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.680124044418335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.662004828453064},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5472570657730103},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.5433341860771179},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.504824697971344},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.502612829208374},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.47386932373046875},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.4632296562194824},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4433203339576721},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3892270624637604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3673819899559021},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3395084738731384},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14301636815071106},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3605801.3605841","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3605801.3605841","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 2nd International Conference on Networks, Communications and Information Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W2240536489","https://openalex.org/W2790114420","https://openalex.org/W2915806950","https://openalex.org/W2943852858","https://openalex.org/W2973960501","https://openalex.org/W3034377024","https://openalex.org/W3198263212","https://openalex.org/W4224235642","https://openalex.org/W4280623724","https://openalex.org/W6600134738"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2021606399","https://openalex.org/W2611989081","https://openalex.org/W2551731678","https://openalex.org/W4230611425","https://openalex.org/W2731899572","https://openalex.org/W3039257172","https://openalex.org/W4244171230","https://openalex.org/W3032471529","https://openalex.org/W4294635752"],"abstract_inverted_index":{"Wind":[0],"field":[1,21,47,155],"prediction":[2,40,48,148,156],"is":[3],"of":[4,22,78,107],"great":[5],"importance":[6],"for":[7,96,153],"disaster":[8],"prevention,":[9],"mitigation,":[10],"and":[11,34,68,87,123],"various":[12],"commercial":[13],"sectors.":[14],"In":[15],"recent":[16],"years,":[17],"researchers":[18],"in":[19,146],"the":[20,61,65,84,92,104,108,120,125,143],"meteorology":[23],"have":[24],"extensively":[25],"employed":[26],"deep":[27],"learning":[28],"techniques":[29],"to":[30,90],"analyze":[31],"weather":[32,80,134],"elements":[33,81],"time":[35,99],"series":[36],"data,":[37],"yielding":[38],"promising":[39],"results.":[41],"This":[42],"paper":[43],"proposes":[44],"a":[45],"wind":[46,94,154],"model":[49,62,102,121],"based":[50,131],"on":[51,132,139],"graph":[52],"attention":[53],"neural":[54],"networks.":[55],"By":[56],"incorporating":[57],"spatial":[58,66],"geographical":[59],"information,":[60],"effectively":[63],"captures":[64],"relationships":[67,112],"interactions":[69],"among":[70],"meteorological":[71,110,129],"stations.":[72],"It":[73],"utilizes":[74],"historical":[75,133],"hourly":[76],"data":[77],"several":[79],"obtained":[82],"from":[83],"target":[85],"station":[86,111],"surrounding":[88],"stations":[89,130],"forecast":[91],"average":[93],"speed":[95],"multiple":[97],"future":[98],"steps.":[100],"The":[101],"optimizes":[103],"adjacency":[105],"matrix":[106],"relevant":[109],"along":[113],"with":[114],"other":[115],"features.":[116],"Through":[117],"this":[118],"approach,":[119],"learns":[122],"determines":[124],"connections":[126],"between":[127],"neighboring":[128],"element":[135],"data.":[136],"Numerical":[137],"experiments":[138],"real-world":[140],"datasets":[141],"demonstrate":[142],"model's":[144],"effectiveness":[145],"improving":[147],"accuracy,":[149],"providing":[150],"valuable":[151],"insights":[152],"tasks.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}