{"id":"https://openalex.org/W7155410126","doi":"https://doi.org/10.1145/3777884.3797006","title":"A Comparative Evaluation of Imputation Models for Agricultural Weather Networks","display_name":"A Comparative Evaluation of Imputation Models for Agricultural Weather Networks","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155410126","doi":"https://doi.org/10.1145/3777884.3797006"},"language":null,"primary_location":{"id":"doi:10.1145/3777884.3797006","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777884.3797006","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM/SPEC International Conference on Performance Engineering","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3777884.3797006","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134399082","display_name":"Awanish Khanal","orcid":"https://orcid.org/0009-0005-4858-4265"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Awanish Khanal","raw_affiliation_strings":["Washington State University, Pullman, USA"],"raw_orcid":"https://orcid.org/0009-0005-4858-4265","affiliations":[{"raw_affiliation_string":"Washington State University, Pullman, USA","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032176749","display_name":"Monowar Hasan","orcid":"https://orcid.org/0000-0002-2657-0402"},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Monowar Hasan","raw_affiliation_strings":["Washington State University, Pullman, USA"],"raw_orcid":"https://orcid.org/0000-0002-2657-0402","affiliations":[{"raw_affiliation_string":"Washington State University, Pullman, USA","institution_ids":["https://openalex.org/I72951846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5134399082"],"corresponding_institution_ids":["https://openalex.org/I72951846"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.9188898,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"230","last_page":"241"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.2653999924659729,"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"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.2653999924659729,"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"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.10840000212192535,"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/T11234","display_name":"Precipitation Measurement and Analysis","score":0.08609999716281891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6281999945640564},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.54339998960495},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.5246000289916992},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.505299985408783},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4499000012874603},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4325000047683716},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.3831000030040741}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345000267028809},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6281999945640564},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.54339998960495},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4499000012874603},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43860000371932983},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3856000006198883},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C180591934","wikidata":"https://www.wikidata.org/wiki/Q1253369","display_name":"Downtime","level":2,"score":0.3490999937057495},{"id":"https://openalex.org/C116019233","wikidata":"https://www.wikidata.org/wiki/Q186205","display_name":"Food energy","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C128383755","wikidata":"https://www.wikidata.org/wiki/Q3816336","display_name":"Agricultural productivity","level":3,"score":0.29120001196861267},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26809999346733093},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.25529998540878296},{"id":"https://openalex.org/C2777589951","wikidata":"https://www.wikidata.org/wiki/Q6073845","display_name":"Irrigation scheduling","level":3,"score":0.2547000050544739}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3777884.3797006","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777884.3797006","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM/SPEC International Conference on Performance Engineering","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3777884.3797006","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3777884.3797006","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 17th ACM/SPEC International Conference on Performance Engineering","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.6844155788421631}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1667277804","https://openalex.org/W1984727442","https://openalex.org/W2064186732","https://openalex.org/W2072697706","https://openalex.org/W2091012872","https://openalex.org/W2091604069","https://openalex.org/W2096863518","https://openalex.org/W2139086914","https://openalex.org/W2149137452","https://openalex.org/W2177109858","https://openalex.org/W2200439697","https://openalex.org/W2576479479","https://openalex.org/W2911964244","https://openalex.org/W2964010366","https://openalex.org/W2982102626","https://openalex.org/W4323051310"],"related_works":[],"abstract_inverted_index":{"High-resolution":[0],"weather":[1,17,204],"data":[2,95,205],"are":[3,60,137],"essential":[4],"for":[5,29,63,87,201],"irrigation":[6],"scheduling,":[7],"frost":[8],"protection,":[9],"and":[10,12,51,78,104,132,142,165,180],"pest":[11],"disease":[13],"risk":[14],"modeling.":[15],"However,":[16],"stations":[18],"frequently":[19],"experience":[20],"multi-hour":[21],"to":[22,26,41,118],"multi-day":[23],"outages,":[24],"leading":[25],"substantial":[27],"downtime":[28],"weather-driven":[30],"decision-making.":[31],"To":[32],"mitigate":[33],"this,":[34],"stakeholders":[35],"often":[36],"rely":[37],"on":[38],"''imputation''":[39],"models":[40,93,196],"reconstruct":[42],"missing":[43],"data.":[44],"Despite":[45],"the":[46,83,92],"existence":[47],"of":[48],"many":[49],"statistical":[50],"machine-learning":[52],"models,":[53,73,146],"it":[54],"remains":[55],"unclear":[56],"which":[57],"imputation":[58,71,206],"methods":[59],"most":[61,84],"suitable":[62,85],"operational":[64],"agricultural":[65,88,208],"settings.":[66],"This":[67],"paper":[68],"evaluates":[69],"twelve":[70],"methods---statistical":[72],"classical":[74,193],"machine":[75,194],"learning":[76,187,195],"algorithms,":[77],"deep":[79,186],"neural":[80],"networks---to":[81],"identify":[82],"model":[86,153],"applications.":[89],"We":[90,110],"tested":[91],"using":[94],"from":[96],"five":[97],"meteorological":[98],"towers":[99],"(three":[100],"in":[101,106,116,207],"Jena,":[102],"Germany,":[103],"two":[105],"Sunnyside,":[107],"Washington,":[108],"USA).":[109],"perform":[111],"a":[112,147,198],"thorough":[113],"performance-engineering":[114],"study:":[115],"addition":[117],"accuracy,":[119,162],"we":[120],"evaluate":[121],"runtime,":[122],"inference":[123],"throughput,":[124,164],"peak":[125],"memory":[126,178],"usage,":[127,129,179],"GPU":[128,175],"energy":[130,183],"consumption,":[131],"monetary":[133],"cost.":[134],"Our":[135,189],"findings":[136],"surprising:":[138],"among":[139],"all":[140],"complex":[141],"advanced":[143],"(neural":[144],"network)":[145],"properly":[148],"tuned":[149],"Random":[150],"Forest":[151],"(RF)":[152],"consistently":[154],"outperforms":[155],"them":[156],"across":[157],"multiple":[158],"evaluation":[159],"categories":[160],"(e.g.,":[161],"latency,":[163],"cost).":[166],"Specifically,":[167],"an":[168],"RF":[169],"achieves":[170],"competitive":[171],"error":[172],"with":[173],"no":[174],"dependencies,":[176],"modest":[177],"substantially":[181],"lower":[182],"expenditure":[184],"than":[185],"baselines.":[188],"research":[190],"shows":[191],"that":[192],"remain":[197],"compelling":[199],"choice":[200],"scalable,":[202],"cost-aware":[203],"decision-support":[209],"systems.":[210]},"counts_by_year":[],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2026-04-24T00:00:00"}