{"id":"https://openalex.org/W7130403752","doi":"https://doi.org/10.1109/access.2026.3665921","title":"Climate-Sensitive Modeling of Rice Stock in Odisha Using Hybrid Machine Learning and Explainable AI","display_name":"Climate-Sensitive Modeling of Rice Stock in Odisha Using Hybrid Machine Learning and Explainable AI","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7130403752","doi":"https://doi.org/10.1109/access.2026.3665921"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3665921","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3665921","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3665921","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114233681","display_name":"Nabin Krishna Mohanty","orcid":null},"institutions":[{"id":"https://openalex.org/I67357951","display_name":"KIIT University","ror":"https://ror.org/00k8zt527","country_code":"IN","type":"education","lineage":["https://openalex.org/I67357951"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nabin Krishna Mohanty","raw_affiliation_strings":["School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, India","institution_ids":["https://openalex.org/I67357951"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Pradeep Kumar Mallick","orcid":"https://orcid.org/0000-0002-1207-0757"},"institutions":[{"id":"https://openalex.org/I67357951","display_name":"KIIT University","ror":"https://ror.org/00k8zt527","country_code":"IN","type":"education","lineage":["https://openalex.org/I67357951"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pradeep Kumar Mallick","raw_affiliation_strings":["School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, India"],"raw_orcid":"https://orcid.org/0000-0002-1207-0757","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, India","institution_ids":["https://openalex.org/I67357951"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028973193","display_name":"Hrudaya Kumar Tripathy","orcid":"https://orcid.org/0000-0002-4960-6228"},"institutions":[{"id":"https://openalex.org/I67357951","display_name":"KIIT University","ror":"https://ror.org/00k8zt527","country_code":"IN","type":"education","lineage":["https://openalex.org/I67357951"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Hrudaya Kumar Tripathy","raw_affiliation_strings":["School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, India"],"raw_orcid":"https://orcid.org/0000-0002-4960-6228","affiliations":[{"raw_affiliation_string":"School of Computer Engineering, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, India","institution_ids":["https://openalex.org/I67357951"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I67357951"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":14.6677,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.96589312,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"14","issue":null,"first_page":"28785","last_page":"28805"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10439","display_name":"Climate change impacts on agriculture","score":0.33889999985694885,"subfield":{"id":"https://openalex.org/subfields/1105","display_name":"Ecology, Evolution, Behavior and Systematics"},"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/T10439","display_name":"Climate change impacts on agriculture","score":0.33889999985694885,"subfield":{"id":"https://openalex.org/subfields/1105","display_name":"Ecology, Evolution, Behavior and Systematics"},"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/T10616","display_name":"Smart Agriculture and AI","score":0.11900000274181366,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.11550000309944153,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5461000204086304},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.5313000082969666},{"id":"https://openalex.org/keywords/evapotranspiration","display_name":"Evapotranspiration","score":0.48080000281333923},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.38109999895095825},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.37389999628067017},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3465999960899353},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3433000147342682},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.32269999384880066},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.32190001010894775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6148999929428101},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5864999890327454},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5461000204086304},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.5313000082969666},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4839000105857849},{"id":"https://openalex.org/C176783924","wikidata":"https://www.wikidata.org/wiki/Q828158","display_name":"Evapotranspiration","level":2,"score":0.48080000281333923},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.38109999895095825},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3531999886035919},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.3366999924182892},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.30059999227523804},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C50897621","wikidata":"https://www.wikidata.org/wiki/Q2665508","display_name":"Hybrid system","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2685000002384186},{"id":"https://openalex.org/C126343540","wikidata":"https://www.wikidata.org/wiki/Q889514","display_name":"Crop yield","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2777589951","wikidata":"https://www.wikidata.org/wiki/Q6073845","display_name":"Irrigation scheduling","level":3,"score":0.26339998841285706},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C88862950","wikidata":"https://www.wikidata.org/wiki/Q11453","display_name":"Irrigation","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C168754636","wikidata":"https://www.wikidata.org/wiki/Q620920","display_name":"Climate model","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3665921","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3665921","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3665921","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3665921","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.8121321797370911}],"awards":[],"funders":[{"id":"https://openalex.org/F4320331003","display_name":"Kalinga Institute of Industrial Technology","ror":"https://ror.org/00k8zt527"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W2025955683","https://openalex.org/W2077766806","https://openalex.org/W2137256004","https://openalex.org/W2181460351","https://openalex.org/W3197898438","https://openalex.org/W3210649844","https://openalex.org/W4200397128","https://openalex.org/W4205846668","https://openalex.org/W4210362982","https://openalex.org/W4229014251","https://openalex.org/W4376598698","https://openalex.org/W4378472958","https://openalex.org/W4383619745","https://openalex.org/W4385470126","https://openalex.org/W4385626381","https://openalex.org/W4385696146","https://openalex.org/W4390738897","https://openalex.org/W4392594314","https://openalex.org/W4392741075","https://openalex.org/W4396851214","https://openalex.org/W4400645063","https://openalex.org/W4403918849","https://openalex.org/W4404103089","https://openalex.org/W4404311431","https://openalex.org/W4405219175","https://openalex.org/W4405369919","https://openalex.org/W4405414079","https://openalex.org/W4406942883","https://openalex.org/W4407878535","https://openalex.org/W4408743034","https://openalex.org/W4408996043","https://openalex.org/W4409451415","https://openalex.org/W4409475265","https://openalex.org/W4409797242","https://openalex.org/W4410253183","https://openalex.org/W4410384669","https://openalex.org/W4410530033","https://openalex.org/W4411143919","https://openalex.org/W4411371159"],"related_works":[],"abstract_inverted_index":{"Climate":[0],"variability":[1],"has":[2],"increased":[3],"uncertainty":[4],"in":[5,56,109,230],"agricultural":[6],"production":[7],"and":[8,24,81,92,105,122,146,169,181,190,210,225],"post-harvest":[9],"management,":[10,224],"yet":[11],"most":[12],"existing":[13],"studies":[14],"focus":[15],"on":[16,27],"average":[17],"crop":[18],"yields":[19],"at":[20],"aggregated":[21],"spatial":[22],"scales":[23],"rely":[25],"either":[26],"linear":[28,96],"statistical":[29,142],"models":[30,38,150],"with":[31,39,126],"limited":[32],"flexibility":[33],"or":[34],"black-box":[35],"machine":[36,148],"learning":[37,149],"poor":[40],"interpretability.":[41,82],"As":[42],"a":[43,71,95,152],"result,":[44],"district-level":[45,127],"forecasting":[46,74],"of":[47,135,172],"climate-sensitive":[48],"rice":[49,110,128,227],"stock":[50,111,129,195,228],"dynamics":[51],"remains":[52],"insufficiently":[53],"addressed,":[54],"particularly":[55],"highly":[57],"vulnerable":[58],"regions":[59],"such":[60,216],"as":[61,184,217],"Odisha,":[62],"India.":[63],"This":[64],"study":[65],"addresses":[66],"this":[67],"gap":[68],"by":[69],"developing":[70],"district-level,":[72],"climate-aware":[73,205],"framework":[75],"that":[76,158,204],"jointly":[77],"improves":[78],"accuracy,":[79],"robustness,":[80],"A":[83],"stacked":[84],"hybrid":[85,161,206],"model":[86,138,162],"integrating":[87],"an":[88,170],"Artificial":[89],"Neural":[90],"Network":[91],"XGBoost":[93],"through":[94],"meta-regressor":[97],"is":[98,139],"proposed":[99,160],"to":[100],"capture":[101],"nonlinear":[102],"climate":[103],"effects":[104],"structured":[106],"residual":[107],"patterns":[108],"variability.":[112],"Monthly":[113],"agro-climatic":[114,133],"variables":[115],"including":[116],"temperature,":[117],"rainfall,":[118],"humidity,":[119],"wind":[120],"speed,":[121],"evapotranspiration":[123,180],"are":[124],"combined":[125],"data":[130],"for":[131],"representative":[132],"zones":[134],"Odisha.":[136],"The":[137],"evaluated":[140],"against":[141],"baselines,":[143],"time-series":[144],"benchmarks,":[145],"standalone":[147],"using":[151],"time-aware":[153],"validation":[154],"strategy.":[155],"Results":[156],"show":[157],"the":[159],"outperforms":[163],"all":[164],"comparators,":[165],"achieving":[166],"anR2of":[167],"0.9727":[168],"MSE":[171],"1.73":[173],"\u00d7":[174],"10\u2079":[175],".":[176],"SHAP-based":[177],"explainability":[178],"identifies":[179],"maximum":[182],"temperature":[183],"dominant":[185],"drivers,":[186],"while":[187],"high":[188],"humidity":[189],"cumulative":[191],"rainfall":[192],"negatively":[193],"affect":[194],"stability,":[196],"especially":[197],"under":[198],"extreme":[199],"conditions.":[200],"These":[201],"findings":[202],"demonstrate":[203],"modelling":[207],"provides":[208],"reliable":[209],"interpretable":[211],"forecasts,":[212],"supporting":[213],"practical":[214],"applications":[215],"adaptive":[218],"sowing":[219],"decisions,":[220],"irrigation":[221],"planning,":[222],"storage":[223],"evidence-based":[226],"regulation":[229],"climate-vulnerable":[231],"regions.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-02-19T00:00:00"}