{"id":"https://openalex.org/W7124437901","doi":"https://doi.org/10.1016/j.ecoinf.2025.103593","title":"Informer-based cross-site transfer learning for water demand forecasting via domain adaptation and meta-learning","display_name":"Informer-based cross-site transfer learning for water demand forecasting via domain adaptation and meta-learning","publication_year":2026,"publication_date":"2026-01-16","ids":{"openalex":"https://openalex.org/W7124437901","doi":"https://doi.org/10.1016/j.ecoinf.2025.103593"},"language":"en","primary_location":{"id":"doi:10.1016/j.ecoinf.2025.103593","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2025.103593","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.ecoinf.2025.103593","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064302239","display_name":"Nevena Rankovi\u0107","orcid":"https://orcid.org/0000-0002-9910-5886"},"institutions":[{"id":"https://openalex.org/I193700539","display_name":"Tilburg University","ror":"https://ror.org/04b8v1s79","country_code":"NL","type":"education","lineage":["https://openalex.org/I193700539"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Nevena Rankovi\u0107","raw_affiliation_strings":["Department of Intelligent Systems, Tilburg School of Humanities and Digital Sciences, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, North-Brabant, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0002-9910-5886","affiliations":[{"raw_affiliation_string":"Department of Intelligent Systems, Tilburg School of Humanities and Digital Sciences, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, North-Brabant, The Netherlands","institution_ids":["https://openalex.org/I193700539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006083376","display_name":"Dragica Rankovi\u0107","orcid":"https://orcid.org/0000-0002-4464-0726"},"institutions":[{"id":"https://openalex.org/I4210105304","display_name":"Union University","ror":"https://ror.org/01p8d4t94","country_code":"RS","type":"education","lineage":["https://openalex.org/I4210105304"]}],"countries":["RS"],"is_corresponding":false,"raw_author_name":"Dragica Rankovi\u0107","raw_affiliation_strings":["Department of informatics, School of Computing, Union University, Knez Mihajlova 6, Belgrade, 11 000, Serbia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of informatics, School of Computing, Union University, Knez Mihajlova 6, Belgrade, 11 000, Serbia","institution_ids":["https://openalex.org/I4210105304"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5064302239"],"corresponding_institution_ids":["https://openalex.org/I193700539"],"apc_list":{"value":2510,"currency":"USD","value_usd":2510},"apc_paid":{"value":2510,"currency":"USD","value_usd":2510},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08528664,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"93","issue":null,"first_page":"103593","last_page":"103593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11220","display_name":"Water Systems and Optimization","score":0.7875999808311462,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11220","display_name":"Water Systems and Optimization","score":0.7875999808311462,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T10969","display_name":"Water resources management and optimization","score":0.13570000231266022,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.011500000022351742,"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/scada","display_name":"SCADA","score":0.6351000070571899},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5468999743461609},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.542900025844574},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5297999978065491},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.49459999799728394},{"id":"https://openalex.org/keywords/resilience","display_name":"Resilience (materials science)","score":0.46959999203681946},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.45500001311302185},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.45320001244544983},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.421099990606308}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6963000297546387},{"id":"https://openalex.org/C113863187","wikidata":"https://www.wikidata.org/wiki/Q17498","display_name":"SCADA","level":2,"score":0.6351000070571899},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5468999743461609},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.542900025844574},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5297999978065491},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.49459999799728394},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.46959999203681946},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.45500001311302185},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C97053079","wikidata":"https://www.wikidata.org/wiki/Q1061108","display_name":"Water supply","level":2,"score":0.40630000829696655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.390500009059906},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C49261128","wikidata":"https://www.wikidata.org/wiki/Q1132455","display_name":"Hazard","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C137176749","wikidata":"https://www.wikidata.org/wiki/Q4105337","display_name":"Psychological resilience","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.31139999628067017},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2989000082015991},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2939999997615814},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.28130000829696655},{"id":"https://openalex.org/C2992151728","wikidata":"https://www.wikidata.org/wiki/Q474883","display_name":"Water utility","level":3,"score":0.27639999985694885},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.25929999351501465}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1016/j.ecoinf.2025.103593","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2025.103593","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"},{"id":"pmh:oai:tilburguniversity.edu:openaire/d947d97b-b922-498b-bb89-f187fd98ee8f","is_oa":true,"landing_page_url":"https://research.tilburguniversity.edu/en/publications/d947d97b-b922-498b-bb89-f187fd98ee8f","pdf_url":null,"source":{"id":"https://openalex.org/S4406923027","display_name":"Tilburg University Research Portal","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Rankovi\u0107, N & Rankovic, D 2026, 'Informer-based cross-site transfer learning for water demand forecasting via domain adaptation and meta-learning', Ecological Informatics, vol. 93, 103593. https://doi.org/10.1016/j.ecoinf.2025.103593","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:doaj.org/article:9416b622035746debea5ba8e2ab30363","is_oa":true,"landing_page_url":"https://doaj.org/article/9416b622035746debea5ba8e2ab30363","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":"Ecological Informatics, Vol 93, Iss , Pp 103593- (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1016/j.ecoinf.2025.103593","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.ecoinf.2025.103593","pdf_url":null,"source":{"id":"https://openalex.org/S195809937","display_name":"Ecological Informatics","issn_l":"1574-9541","issn":["1574-9541","1878-0512"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Ecological Informatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W2911964244","https://openalex.org/W3022643593","https://openalex.org/W3121975202","https://openalex.org/W3146796711","https://openalex.org/W3148723980","https://openalex.org/W3161667249","https://openalex.org/W3177318507","https://openalex.org/W3181620959","https://openalex.org/W3206853280","https://openalex.org/W4200547309","https://openalex.org/W4210256928","https://openalex.org/W4280581965","https://openalex.org/W4284978044","https://openalex.org/W4311630596","https://openalex.org/W4317802907","https://openalex.org/W4322760294","https://openalex.org/W4324146048","https://openalex.org/W4378697395","https://openalex.org/W4380150152","https://openalex.org/W4385650496","https://openalex.org/W4385666331","https://openalex.org/W4386712244","https://openalex.org/W4387071604","https://openalex.org/W4387365365","https://openalex.org/W4388817564","https://openalex.org/W4388818116","https://openalex.org/W4391361008","https://openalex.org/W4392747119","https://openalex.org/W4393256637","https://openalex.org/W4400033304","https://openalex.org/W4400066904","https://openalex.org/W4401009811","https://openalex.org/W4403102816","https://openalex.org/W4404002773","https://openalex.org/W4405455636","https://openalex.org/W4406220020","https://openalex.org/W4406457754","https://openalex.org/W4407633107","https://openalex.org/W4412352766","https://openalex.org/W4412368979","https://openalex.org/W4412567246","https://openalex.org/W4413364583","https://openalex.org/W4413776989"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"forecasting":[1,17,118,228,277],"of":[2,12,37,158,268],"water":[3,72,137,194,273],"demand":[4,128],"is":[5],"essential":[6],"for":[7,64,136,193],"reliable":[8],"and":[9,26,53,90,101,114,130,164,179,213,215,223,245],"efficient":[10],"operation":[11],"distribution":[13],"networks.":[14],"However,":[15],"existing":[16],"approaches":[18],"are":[19,124],"usually":[20],"restricted":[21],"to":[22,94,127,202,258],"single-site":[23],"closed":[24],"settings":[25],"fail":[27],"under":[28,106,233],"distributional":[29],"shifts":[30],"across":[31,117,278],"pumping":[32,204,280],"stations,":[33],"leaving":[34],"the":[35,44,60,70,79,183],"gap":[36],"cross-site":[38,186],"generalization":[39],"unresolved.":[40],"This":[41],"study":[42,184],"addresses":[43],"problem":[45],"using":[46,176],"operational":[47,211],"data":[48],"collected":[49],"from":[50],"Supervisory":[51],"Control":[52],"Data":[54],"Acquisition":[55],"(SCADA)":[56],"systems":[57],"provided":[58],"by":[59],"Public":[61],"Utility":[62],"Company":[63],"Water":[65],"Treatment":[66],"Valjevo,":[67],"which":[68],"operates":[69],"Kolubara":[71],"supply":[73],"region":[74],"in":[75,219],"Serbia.":[76],"We":[77],"extend":[78],"Informer":[80,237],"architecture":[81],"with":[82,154,206,240,255],"statistical":[83],"alignment":[84,143],"(CORAL,":[85],"MMD),":[86],"adversarial":[87],"adaptation":[88,153,232],"(DANN),":[89],"meta-learning":[91,150],"(MAML,":[92],"Reptile)":[93],"explicitly":[95],"handle":[96],"zero":[97,146],",":[98,100],"few":[99,252],"full":[102],"shot":[103],"transfer":[104,187],"scenarios":[105],"covariate":[107,234],"shift.":[108,235],"The":[109,167],"proposed":[110],"framework":[111],"reduces":[112],"mean":[113],"peak":[115],"errors":[116],"horizons,":[119],"improving":[120],"resilience":[121],"where":[122],"operations":[123],"most":[125],"vulnerable":[126],"surges,":[129],"thus":[131],"carries":[132],"direct":[133],"social":[134],"relevance":[135],"security.":[138],"Results":[139],"show":[140],"that":[141,200],"statistical/adversarial":[142],"enables":[144,250],"effective":[145],"-shot":[147,253],"transfer,":[148],"while":[149,209],"supports":[151],"rapid":[152,251],"only":[155,256],"24\u201372":[156],"h":[157],"labeled":[159,260],"data,":[160,173],"consistently":[161],"outperforming":[162],"classical":[163],"deep-learning":[165],"baselines.":[166],"evaluation,":[168],"conducted":[169],"on":[170],"multivariate":[171],"SCADA":[172],"was":[174],"checked":[175],"standard":[177],"accuracy":[178],"peak-sensitive":[180],"metrics.":[181],"Generally,":[182],"establishes":[185],"learning":[188,254],"as":[189,230],"an":[190],"engineering":[191],"solution":[192],"utilities,":[195],"offering":[196],"a":[197],"deployable":[198],"pipeline":[199],"adapts":[201],"new":[203],"stations":[205],"minimal":[207],"calibration":[208],"reducing":[210],"risks":[212],"costs":[214],"grounding":[216],"its":[217],"value":[218],"both":[220],"methodological":[221],"innovation":[222],"empirical":[224],"validation.":[225],"\u2022":[226,236,248,262,271],"Cross-site":[227],"framed":[229],"domain":[231],"backbone":[238],"extended":[239],"CORAL,":[241],"MMD,":[242],"DANN,":[243],"MAML,":[244],"Reptile":[246],"variants.":[247],"Meta-adaptation":[249],"24":[257],"72":[259],"hours.":[261],"Residual":[263],"ACF/PACF":[264],"diagnostics":[265],"confirm":[266],"removal":[267],"domain-specific":[269],"autocorrelation.":[270],"Supports":[272],"security":[274],"through":[275],"adaptive":[276],"heterogeneous":[279],"sites.":[281]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2026-01-17T00:00:00"}
