{"id":"https://openalex.org/W4400764101","doi":"https://doi.org/10.3390/make6030079","title":"Extending Multi-Output Methods for Long-Term Aboveground Biomass Time Series Forecasting Using Convolutional Neural Networks","display_name":"Extending Multi-Output Methods for Long-Term Aboveground Biomass Time Series Forecasting Using Convolutional Neural Networks","publication_year":2024,"publication_date":"2024-07-17","ids":{"openalex":"https://openalex.org/W4400764101","doi":"https://doi.org/10.3390/make6030079"},"language":"en","primary_location":{"id":"doi:10.3390/make6030079","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6030079","pdf_url":"https://www.mdpi.com/2504-4990/6/3/79/pdf?version=1721280315","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/6/3/79/pdf?version=1721280315","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5099078521","display_name":"Efrain Noa\u2010Yarasca","orcid":"https://orcid.org/0000-0002-5228-8486"},"institutions":[{"id":"https://openalex.org/I4210165003","display_name":"Grassland, Soil and Water Research Laboratory","ror":"https://ror.org/05mfs3k63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1312222531","https://openalex.org/I1336096307","https://openalex.org/I4210145274","https://openalex.org/I4210165003"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Efrain Noa-Yarasca","raw_affiliation_strings":["Texas A&M AgriLife Research, Blackland Research and Extension Center, Temple, TX 76502, USA"],"raw_orcid":"https://orcid.org/0000-0002-5228-8486","affiliations":[{"raw_affiliation_string":"Texas A&M AgriLife Research, Blackland Research and Extension Center, Temple, TX 76502, USA","institution_ids":["https://openalex.org/I4210165003"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014302783","display_name":"Javier M. Osorio Leyton","orcid":"https://orcid.org/0000-0003-4573-4932"},"institutions":[{"id":"https://openalex.org/I4210165003","display_name":"Grassland, Soil and Water Research Laboratory","ror":"https://ror.org/05mfs3k63","country_code":"US","type":"facility","lineage":["https://openalex.org/I1312222531","https://openalex.org/I1336096307","https://openalex.org/I4210145274","https://openalex.org/I4210165003"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Javier M. Osorio Leyton","raw_affiliation_strings":["Texas A&M AgriLife Research, Blackland Research and Extension Center, Temple, TX 76502, USA"],"raw_orcid":"https://orcid.org/0000-0003-4573-4932","affiliations":[{"raw_affiliation_string":"Texas A&M AgriLife Research, Blackland Research and Extension Center, Temple, TX 76502, USA","institution_ids":["https://openalex.org/I4210165003"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046676421","display_name":"Jay P. Angerer","orcid":"https://orcid.org/0000-0003-0988-7181"},"institutions":[{"id":"https://openalex.org/I1312222531","display_name":"Agricultural Research Service","ror":"https://ror.org/02d2m2044","country_code":"US","type":"government","lineage":["https://openalex.org/I1312222531","https://openalex.org/I1336096307"]},{"id":"https://openalex.org/I4210159686","display_name":"Fort Keogh Livestock and Range Research Laboratory","ror":"https://ror.org/05xy09e29","country_code":"US","type":"facility","lineage":["https://openalex.org/I1312222531","https://openalex.org/I1336096307","https://openalex.org/I4210145274","https://openalex.org/I4210159686"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jay P. Angerer","raw_affiliation_strings":["USDA Agricultural Research Service\u2014Livestock and Range Research Laboratory, Miles City, MT 59301, USA"],"raw_orcid":"https://orcid.org/0000-0003-0988-7181","affiliations":[{"raw_affiliation_string":"USDA Agricultural Research Service\u2014Livestock and Range Research Laboratory, Miles City, MT 59301, USA","institution_ids":["https://openalex.org/I4210159686","https://openalex.org/I1312222531"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5099078521"],"corresponding_institution_ids":["https://openalex.org/I4210165003"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":3.5225,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.92141821,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"6","issue":"3","first_page":"1633","last_page":"1652"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.98580002784729,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.98580002784729,"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/T10555","display_name":"Fire effects on ecosystems","score":0.9846000075340271,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9735999703407288,"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/term","display_name":"Term (time)","score":0.620165228843689},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6087689399719238},{"id":"https://openalex.org/keywords/biomass","display_name":"Biomass (ecology)","score":0.5461878776550293},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5274078249931335},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5224855542182922},{"id":"https://openalex.org/keywords/time-horizon","display_name":"Time horizon","score":0.46896329522132874},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4285759925842285},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4182281196117401},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.36894887685775757},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24727743864059448},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.24463996291160583},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.17823776602745056},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1738794445991516},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.1481403112411499}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.620165228843689},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6087689399719238},{"id":"https://openalex.org/C115540264","wikidata":"https://www.wikidata.org/wiki/Q2945560","display_name":"Biomass (ecology)","level":2,"score":0.5461878776550293},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5274078249931335},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5224855542182922},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.46896329522132874},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4285759925842285},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4182281196117401},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.36894887685775757},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24727743864059448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24463996291160583},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.17823776602745056},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1738794445991516},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.1481403112411499},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make6030079","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6030079","pdf_url":"https://www.mdpi.com/2504-4990/6/3/79/pdf?version=1721280315","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:046d849d9b054cbabd46b7db918085c2","is_oa":true,"landing_page_url":"https://doaj.org/article/046d849d9b054cbabd46b7db918085c2","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Machine Learning and Knowledge Extraction, Vol 6, Iss 3, Pp 1633-1652 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make6030079","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6030079","pdf_url":"https://www.mdpi.com/2504-4990/6/3/79/pdf?version=1721280315","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4400764101.pdf"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W777046221","https://openalex.org/W1527561456","https://openalex.org/W1553316618","https://openalex.org/W1784922301","https://openalex.org/W2003706483","https://openalex.org/W2012787632","https://openalex.org/W2014928429","https://openalex.org/W2031489288","https://openalex.org/W2067688816","https://openalex.org/W2074121977","https://openalex.org/W2294097774","https://openalex.org/W2560409612","https://openalex.org/W2775477417","https://openalex.org/W2783048434","https://openalex.org/W2897257904","https://openalex.org/W2898461917","https://openalex.org/W2914305340","https://openalex.org/W2942137667","https://openalex.org/W2955953739","https://openalex.org/W3004905223","https://openalex.org/W3006320604","https://openalex.org/W3026399393","https://openalex.org/W3082741340","https://openalex.org/W3123760665","https://openalex.org/W3136021864","https://openalex.org/W3192393044","https://openalex.org/W4206262190","https://openalex.org/W4239854197","https://openalex.org/W4241650938","https://openalex.org/W4251197426","https://openalex.org/W4289258409","https://openalex.org/W4294018497","https://openalex.org/W4295973414","https://openalex.org/W4304806902","https://openalex.org/W4307890919","https://openalex.org/W4308969770","https://openalex.org/W4317397632","https://openalex.org/W4317738931","https://openalex.org/W4386160141","https://openalex.org/W4391644038","https://openalex.org/W6633039821","https://openalex.org/W6846411258"],"related_works":["https://openalex.org/W2357124094","https://openalex.org/W2387399993","https://openalex.org/W2389739210","https://openalex.org/W2348924972","https://openalex.org/W2365736347","https://openalex.org/W2047454415","https://openalex.org/W2070040999","https://openalex.org/W2387293848","https://openalex.org/W4387399830","https://openalex.org/W1994643058"],"abstract_inverted_index":{"Accurate":[0],"aboveground":[1,31,50],"vegetation":[2,51],"biomass":[3,32,71,127],"forecasting":[4,49],"is":[5,59],"essential":[6],"for":[7,48,99,126,144,188,244],"livestock":[8],"management,":[9],"climate":[10],"impact":[11,134],"assessments,":[12],"and":[13,43,64,73,83,85,89,105,116,148,178,218,233,241],"ecosystem":[14],"health.":[15],"While":[16],"artificial":[17],"intelligence":[18],"(AI)":[19],"techniques":[20],"have":[21],"advanced":[22],"time":[23,33],"series":[24,34],"forecasting,":[25],"a":[26,109,123],"research":[27,226],"gap":[28],"in":[29,112,154,176],"predicting":[30,206],"beyond":[35],"single":[36],"values":[37],"persists.":[38],"This":[39,129,210],"study":[40,130,211],"introduces":[41],"RECMO":[42,115,217],"DirRecMO,":[44],"two":[45],"multi-output":[46,86,106,120,136,231],"methods":[47,80,87,96,107],"biomass.":[52],"Using":[53],"convolutional":[54],"neural":[55],"networks,":[56],"their":[57],"efficacy":[58],"evaluated":[60],"across":[61,199,237],"short-,":[62],"medium-,":[63],"long-term":[65,113,155,179,189],"horizons":[66],"on":[67,138],"six":[68],"Kenyan":[69],"grassland":[70],"datasets,":[72,240],"compared":[74],"with":[75],"that":[76,94],"of":[77,135,168,185,205],"existing":[78],"single-output":[79,95,104],"(Recursive,":[81],"Direct,":[82],"DirRec)":[84],"(MIMO":[88],"DIRMO).":[90],"The":[91],"results":[92],"indicate":[93],"are":[97],"superior":[98],"short-term":[100],"predictions,":[101],"while":[102],"both":[103],"exhibit":[108],"comparable":[110],"effectiveness":[111],"forecasts.":[114,156],"DirRecMO":[117],"outperform":[118],"established":[119],"methods,":[121,164,201],"demonstrating":[122],"promising":[124],"potential":[125],"forecasting.":[128,224],"underscores":[131],"the":[132,142,150,166,169,182,193,203],"significant":[133,161],"size":[137,146],"forecast":[139,194],"accuracy,":[140],"highlighting":[141],"need":[143],"optimal":[145],"adjustments":[147],"showcasing":[149],"proposed":[151],"methods\u2019":[152],"flexibility":[153],"Short-term":[157],"predictions":[158],"show":[159],"less":[160],"differences":[162],"among":[163],"complicating":[165],"identification":[167],"best":[170],"performer.":[171],"However,":[172],"clear":[173],"distinctions":[174],"emerge":[175],"medium-":[177],"forecasts,":[180],"underscoring":[181],"greater":[183],"importance":[184],"method":[186],"choice":[187],"predictions.":[190],"Moreover,":[191],"as":[192],"horizon":[195,223],"extends,":[196],"errors":[197],"escalate":[198],"all":[200],"reflecting":[202],"challenges":[204],"distant":[207],"future":[208],"periods.":[209],"suggests":[212],"advancing":[213],"hybrid":[214],"models":[215],"(e.g.,":[216],"DirRecMO)":[219],"to":[220],"improve":[221],"extended":[222],"Future":[225],"should":[227],"enhance":[228],"adaptability,":[229],"investigate":[230],"impacts,":[232],"conduct":[234],"comparative":[235],"studies":[236],"diverse":[238],"domains,":[239],"AI":[242],"algorithms":[243],"robust":[245],"insights.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}