{"id":"https://openalex.org/W4312281528","doi":"https://doi.org/10.1109/igarss46834.2022.9883168","title":"Fuel Moisture Content Forecasting Using Long Short-Term Memory(LSTM) Model","display_name":"Fuel Moisture Content Forecasting Using Long Short-Term Memory(LSTM) Model","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312281528","doi":"https://doi.org/10.1109/igarss46834.2022.9883168"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9883168","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883168","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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/A5051784641","display_name":"Zhenyu Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Kang","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu, Sichuan,P. R. China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu, Sichuan,P. R. China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075267751","display_name":"Miao Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Miao Jiao","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu, Sichuan,P. R. China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu, Sichuan,P. R. China,611731","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101545122","display_name":"Zijie Zhou","orcid":"https://orcid.org/0009-0001-4410-339X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zijie Zhou","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu, Sichuan,P. R. China,611731"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China,Chengdu, Sichuan,P. R. China,611731","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.30404105,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"5672","last_page":"5675"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.9998000264167786,"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/T10555","display_name":"Fire effects on ecosystems","score":0.9998000264167786,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9872000217437744,"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/dew-point","display_name":"Dew point","score":0.8327600955963135},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5736016631126404},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5733432173728943},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5177215933799744},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.5160186290740967},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5005605220794678},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.49031880497932434},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4474150240421295},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.42024847865104675},{"id":"https://openalex.org/keywords/atmospheric-model","display_name":"Atmospheric model","score":0.41741642355918884},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.40188083052635193},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.3752012848854065},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3702194094657898},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.30627310276031494},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14382416009902954}],"concepts":[{"id":"https://openalex.org/C82210777","wikidata":"https://www.wikidata.org/wiki/Q178828","display_name":"Dew point","level":2,"score":0.8327600955963135},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5736016631126404},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5733432173728943},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5177215933799744},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5160186290740967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5005605220794678},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.49031880497932434},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4474150240421295},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.42024847865104675},{"id":"https://openalex.org/C118365302","wikidata":"https://www.wikidata.org/wiki/Q4817115","display_name":"Atmospheric model","level":2,"score":0.41741642355918884},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.40188083052635193},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.3752012848854065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3702194094657898},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30627310276031494},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14382416009902954},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9883168","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883168","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"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":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1608631099","https://openalex.org/W1767322088","https://openalex.org/W2100718094","https://openalex.org/W2402268235","https://openalex.org/W2530222969","https://openalex.org/W2655351493","https://openalex.org/W2900639779","https://openalex.org/W2956093647","https://openalex.org/W2973461110","https://openalex.org/W2974449012","https://openalex.org/W2979478456","https://openalex.org/W2997549107","https://openalex.org/W3038338044","https://openalex.org/W3111830742","https://openalex.org/W3134731620","https://openalex.org/W3160003080","https://openalex.org/W3190147628","https://openalex.org/W6637726464"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4288108708","https://openalex.org/W4387163678","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W3173606726","https://openalex.org/W2904359912"],"abstract_inverted_index":{"Fuel":[0],"moisture":[1],"content":[2],"(FMC)":[3],"of":[4,31,135,141,148],"live":[5],"vegetation":[6],"is":[7,46,57,82],"a":[8,47],"crucial":[9],"wildfire":[10,17,40],"risk":[11],"and":[12,66,145],"spread":[13],"rate":[14],"driver.":[15],"Current":[16],"warnings":[18],"generally":[19],"use":[20,73],"historical":[21],"FMC":[22,32,94],"data,":[23],"which":[24],"leads":[25],"to":[26,72,77],"inaccurate":[27],"predictions.":[28],"The":[29,80,133],"accuracy":[30],"forecasting":[33],"can":[34],"provide":[35],"data":[36],"support":[37],"for":[38,59],"assessing":[39],"danger.":[41],"Long":[42],"short-term":[43,67],"memory":[44],"(LSTM)":[45],"special":[48],"recurrent":[49],"neural":[50],"network":[51],"that":[52,140],"learns":[53],"long-term":[54,65],"dependencies.":[55,68],"It":[56],"suitable":[58],"predicting":[60],"time":[61],"series":[62],"with":[63,93,103],"both":[64],"This":[69],"study":[70],"intends":[71],"the":[74,87,90,96,100,136,142,146,156],"LSTM":[75,91,101,137],"model":[76,92,102,138],"forecast":[78],"FMC.":[79],"experiment":[81],"divided":[83],"into":[84],"three":[85],"parts:":[86],"conventional":[88,143],"model;":[89],"as":[95,152],"only":[97],"feature":[98,105,153],"variable;":[99],"seven":[104],"variables,":[106],"included":[107],"FMC,":[108],"maximum":[109,123],"temperature":[110,113,116,121],"(Tmax),":[111],"minimum":[112,128],"(Tmin),":[114],"mean":[115,118],"(Tmean),":[117],"dew":[119],"point":[120],"(Tdmean),":[122],"vapor":[124,129],"pressure":[125,130],"deficit":[126,131],"(VPDmax),":[127],"(VPDmin).":[132],"results":[134],"outperform":[139],"model,":[144],"effect":[147],"selecting":[149],"multiple":[150],"parameters":[151],"variables":[154],"reached":[155],"best":[157],"accuracy.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}