{"id":"https://openalex.org/W7126020949","doi":"https://doi.org/10.30598/barekengvol20iss2pp1659-1674","title":"SOIL MOISTURE PREDICTION USING LSTM AND GRU: UNIVARIATE AND MULTIVARIATE DEEP LEARNING APPROACHES","display_name":"SOIL MOISTURE PREDICTION USING LSTM AND GRU: UNIVARIATE AND MULTIVARIATE DEEP LEARNING APPROACHES","publication_year":2026,"publication_date":"2026-01-26","ids":{"openalex":"https://openalex.org/W7126020949","doi":"https://doi.org/10.30598/barekengvol20iss2pp1659-1674"},"language":"en","primary_location":{"id":"doi:10.30598/barekengvol20iss2pp1659-1674","doi":"https://doi.org/10.30598/barekengvol20iss2pp1659-1674","is_oa":true,"landing_page_url":"https://doi.org/10.30598/barekengvol20iss2pp1659-1674","pdf_url":"https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/20794/12465","source":{"id":"https://openalex.org/S4210230638","display_name":"BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN","issn_l":"1978-7227","issn":["1978-7227","2615-3017"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BAREKENG: Jurnal Ilmu Matematika dan Terapan","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/20794/12465","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114521737","display_name":"Jemsri Stenli Batlajery","orcid":"https://orcid.org/0009-0005-1604-5291","observed_orcids":["https://orcid.org/0009-0005-1604-5291"]},"institutions":[{"id":"https://openalex.org/I21881038","display_name":"IPB University","ror":"https://ror.org/05smgpd89","country_code":"ID","type":"education","lineage":["https://openalex.org/I21881038"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Jemsri Stenli Batlajery","raw_affiliation_strings":["School of Data Science, Department of Mathematics and Computer Science, IPB University, Indonesia"],"raw_orcid":"https://orcid.org/0009-0005-1604-5291","affiliations":[{"raw_affiliation_string":"School of Data Science, Department of Mathematics and Computer Science, IPB University, Indonesia","institution_ids":["https://openalex.org/I21881038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106339764","display_name":"Agus Buono","orcid":"https://orcid.org/0000-0002-4273-0269","observed_orcids":["https://orcid.org/0000-0002-4273-0269"]},"institutions":[{"id":"https://openalex.org/I21881038","display_name":"IPB University","ror":"https://ror.org/05smgpd89","country_code":"ID","type":"education","lineage":["https://openalex.org/I21881038"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Agus Buono","raw_affiliation_strings":["School of Data Science, Department of Mathematics and Computer Science, IPB University, Indonesia"],"raw_orcid":"https://orcid.org/0000-0002-4273-0269","affiliations":[{"raw_affiliation_string":"School of Data Science, Department of Mathematics and Computer Science, IPB University, Indonesia","institution_ids":["https://openalex.org/I21881038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124163776","display_name":"Mushthofa -Mushthofa","orcid":"https://orcid.org/0000-0003-4991-4148","observed_orcids":["https://orcid.org/0000-0003-4991-4148"]},"institutions":[{"id":"https://openalex.org/I21881038","display_name":"IPB University","ror":"https://ror.org/05smgpd89","country_code":"ID","type":"education","lineage":["https://openalex.org/I21881038"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Mushthofa -Mushthofa","raw_affiliation_strings":["School of Data Science, Department of Mathematics and Computer Science, IPB University, Indonesia"],"raw_orcid":"https://orcid.org/0000-0003-4991-4148","affiliations":[{"raw_affiliation_string":"School of Data Science, Department of Mathematics and Computer Science, IPB University, Indonesia","institution_ids":["https://openalex.org/I21881038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21881038"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03382706,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":"2","first_page":"1659","last_page":"1674"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9965999722480774,"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/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.002400000113993883,"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/T10616","display_name":"Smart Agriculture and AI","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/soil-moisture-prediction","display_name":"soil moisture prediction","score":0.9959999918937683},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.703000009059906},{"id":"https://openalex.org/keywords/gru","display_name":"GRU","score":0.8579999804496765},{"id":"https://openalex.org/keywords/univariate-forecasting","display_name":"univariate time series forecasting","score":0.6520000100135803},{"id":"https://openalex.org/keywords/multivariate-time-series-forecasting","display_name":"multivariate time series forecasting","score":0.9120000004768372},{"id":"https://openalex.org/keywords/climatic-variables","display_name":"climatic variables","score":0.7770000100135803},{"id":"https://openalex.org/keywords/east-java","display_name":"East Java","score":0.6510000228881836},{"id":"https://openalex.org/keywords/drought","display_name":"drought","score":0.3709999918937683},{"id":"https://openalex.org/keywords/precision-agriculture","display_name":"precision agriculture","score":0.7139999866485596},{"id":"https://openalex.org/keywords/google-earth-engine","display_name":"Google Earth Engine","score":0.44699999690055847},{"id":"https://openalex.org/keywords/cubic-spline-interpolation","display_name":"cubic spline interpolation","score":0.5709999799728394}],"concepts":[{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.8536999821662903},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7096999883651733},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.44130000472068787},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.42149999737739563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41670000553131104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41130000352859497},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.3790999948978424},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.376800000667572},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C203332170","wikidata":"https://www.wikidata.org/wiki/Q6334079","display_name":"Multivariate interpolation","level":3,"score":0.3741999864578247},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.3508000075817108},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3345000147819519},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C24939127","wikidata":"https://www.wikidata.org/wiki/Q373499","display_name":"Water content","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3109999895095825},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2973000109195709},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C24552861","wikidata":"https://www.wikidata.org/wiki/Q2670177","display_name":"Data assimilation","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C10390562","wikidata":"https://www.wikidata.org/wiki/Q581809","display_name":"Spline (mechanical)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C168754636","wikidata":"https://www.wikidata.org/wiki/Q620920","display_name":"Climate model","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C176864760","wikidata":"https://www.wikidata.org/wiki/Q217651","display_name":"Moisture","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2621999979019165},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.30598/barekengvol20iss2pp1659-1674","doi":"https://doi.org/10.30598/barekengvol20iss2pp1659-1674","is_oa":true,"landing_page_url":"https://doi.org/10.30598/barekengvol20iss2pp1659-1674","pdf_url":"https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/20794/12465","source":{"id":"https://openalex.org/S4210230638","display_name":"BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN","issn_l":"1978-7227","issn":["1978-7227","2615-3017"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BAREKENG: Jurnal Ilmu Matematika dan Terapan","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.30598/barekengvol20iss2pp1659-1674","doi":"https://doi.org/10.30598/barekengvol20iss2pp1659-1674","is_oa":true,"landing_page_url":"https://doi.org/10.30598/barekengvol20iss2pp1659-1674","pdf_url":"https://ojs3.unpatti.ac.id/index.php/barekeng/article/download/20794/12465","source":{"id":"https://openalex.org/S4210230638","display_name":"BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN","issn_l":"1978-7227","issn":["1978-7227","2615-3017"],"is_oa":true,"is_in_doaj":true,"is_core":false,"listed_in":["doaj","jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BAREKENG: Jurnal Ilmu Matematika dan Terapan","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.6875},{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.685699999332428},{"display_name":"Life on land","id":"https://metadata.un.org/sdg/15","score":0.4390000104904175}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7126020949.pdf","grobid_xml":"https://content.openalex.org/works/W7126020949.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1598796236","https://openalex.org/W1924770834","https://openalex.org/W2064675550","https://openalex.org/W2505672291","https://openalex.org/W2740273583","https://openalex.org/W2889920732","https://openalex.org/W2900251524","https://openalex.org/W2905564639","https://openalex.org/W3046476263","https://openalex.org/W3122928643","https://openalex.org/W3168536162","https://openalex.org/W3179559755","https://openalex.org/W3182706339","https://openalex.org/W3193908804","https://openalex.org/W3207444164","https://openalex.org/W3211167621","https://openalex.org/W3216678516","https://openalex.org/W4200149104","https://openalex.org/W4205495647","https://openalex.org/W4206713112","https://openalex.org/W4210370634","https://openalex.org/W4212926215","https://openalex.org/W4308456021","https://openalex.org/W4313839327","https://openalex.org/W4382345441","https://openalex.org/W4387206258"],"related_works":["https://openalex.org/W7167730754","https://openalex.org/W4413104079","https://openalex.org/W4390863482","https://openalex.org/W4362508044","https://openalex.org/W7220212621","https://openalex.org/W4405565068","https://openalex.org/W4392103129","https://openalex.org/W4386161361","https://openalex.org/W4414331846","https://openalex.org/W4406499911"],"abstract_inverted_index":{"Soil":[0,64],"moisture":[1,26,45,65,267],"is":[2],"an":[3],"important":[4],"indicator":[5],"in":[6,24,82,175,207,235,249,258,269],"the":[7,86,132,189,203,222,233],"management":[8],"of":[9,254],"water":[10],"resources,":[11],"precision":[12],"agriculture,":[13],"and":[14,20,53,61,99,118,126,153,192,225,238,261,272],"disaster":[15],"mitigation,":[16],"such":[17],"as":[18,243,245],"drought":[19],"land":[21],"fires.":[22],"Fluctuations":[23],"soil":[25,44,266],"are":[27],"influenced":[28],"by":[29],"various":[30],"climate":[31,74,241,277],"variables,":[32],"requiring":[33],"a":[34,42,111],"reliable":[35],"predictive":[36],"approach":[37,114],"essential.":[38],"This":[39],"research":[40,230],"develops":[41],"daily":[43,214,265],"prediction":[46,268],"model":[47,135,173,187,234],"using":[48,110,275],"Long":[49],"Short-Term":[50],"Memory":[51],"(LSTM)":[52],"Gated":[54],"Recurrent":[55],"Unit":[56],"(GRU)":[57],"algorithms":[58],"with":[59,140],"univariate":[60,171,204,271],"multivariate":[62,133,185,273],"approaches.":[63],"data":[66,75],"were":[67,76,108],"obtained":[68],"from":[69,78,157,279],"Google":[70],"Earth":[71],"Engine,":[72],"while":[73,202],"collected":[77],"10":[79,169],"BMKG":[80],"stations":[81],"East":[83],"Java":[84],"for":[85,115,197,213,264],"period":[87],"2019\u20132024.":[88],"Data":[89],"preprocessing":[90],"includes":[91],"cubic":[92],"spline":[93],"interpolation":[94],"to":[95,102,117,165,199,221],"handle":[96],"missing":[97],"values":[98],"Min-Max":[100],"normalization":[101],"achieve":[103],"uniform":[104],"feature":[105],"scaling.":[106],"Models":[107],"built":[109],"direct":[112],"forecasting":[113],"horizons":[116],"five":[119],"evaluation":[120],"metrics:":[121],"MAE,":[122],"MSE,":[123],"RMSE,":[124],"MAPE,":[125],"R\u00b2.":[127],"The":[128,170,184,216,252],"results":[129],"show":[130],"that":[131],"GRU":[134,186,262],"performs":[136],"best":[137],"at":[138,181],"horizon":[139],"MAE":[141],"=":[142,145,148,151,155],"0.05455,":[143],"MSE":[144],"0.00604,":[146],"RMSE":[147],"0.07539,":[149],"MAPE":[150],"0.19280,":[152],"R\u00b2":[154],"starting":[156],"0.9626":[158],"on":[159,167],"day":[160,168],"1":[161],"(t),":[162],"then":[163],"decreasing":[164],"0.8075":[166],"LSTM":[172,205,260],"excelled":[174],"training":[176,208],"time":[177],"efficiency":[178],"(<400":[179],"seconds)":[180],"most":[182],"stations.":[183,281],"demonstrates":[188],"highest":[190],"accuracy":[191],"stability,":[193],"making":[194,210],"it":[195,211],"suitable":[196],"medium-":[198],"long-term":[200],"forecasting,":[201],"excels":[206],"speed,":[209],"effective":[212],"predictions.":[215],"model\u2019s":[217],"performance":[218,263],"remains":[219],"limited":[220],"dataset's":[223],"spatial":[224],"temporal":[226],"scope.":[227],"Therefore,":[228],"future":[229],"should":[231],"test":[232],"other":[236],"regions":[237],"under":[239],"extreme":[240],"conditions,":[242],"well":[244],"apply":[246],"transfer":[247],"learning":[248],"data-scarce":[250],"areas.":[251],"novelty":[253],"this":[255],"study":[256],"lies":[257],"comparing":[259],"both":[270],"scenarios,":[274],"complete":[276],"variables":[278],"multiple":[280]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-01-30T00:00:00"}
