{"id":"https://openalex.org/W7133223044","doi":"https://doi.org/10.1016/j.jocs.2026.102821","title":"Disturbance storm time index prediction with interpretable machine learning","display_name":"Disturbance storm time index prediction with interpretable machine learning","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133223044","doi":"https://doi.org/10.1016/j.jocs.2026.102821"},"language":"en","primary_location":{"id":"doi:10.1016/j.jocs.2026.102821","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.jocs.2026.102821","pdf_url":null,"source":{"id":"https://openalex.org/S192071280","display_name":"Journal of Computational Science","issn_l":"1877-7503","issn":["1877-7503","1877-7511"],"is_oa":false,"is_in_doaj":false,"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":"Journal of Computational Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.jocs.2026.102821","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118683192","display_name":"Luca Pennati","orcid":"https://orcid.org/0009-0009-4901-1716"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Luca Pennati","raw_affiliation_strings":["Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":"https://orcid.org/0009-0009-4901-1716","affiliations":[{"raw_affiliation_string":"Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047194408","display_name":"Jonah Ekelund","orcid":null},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Jonah Ekelund","raw_affiliation_strings":["Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082554131","display_name":"Andong Hu","orcid":"https://orcid.org/0000-0002-6929-2158"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Andong Hu","raw_affiliation_strings":["Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122360044","display_name":"Ivy Peng","orcid":"https://orcid.org/0000-0003-4158-3583"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Ivy Peng","raw_affiliation_strings":["Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127852039","display_name":"Stefano Markidis","orcid":null},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Stefano Markidis","raw_affiliation_strings":["Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5118683192"],"corresponding_institution_ids":["https://openalex.org/I86987016"],"apc_list":{"value":2810,"currency":"USD","value_usd":2810},"apc_paid":{"value":2810,"currency":"USD","value_usd":2810},"fwci":9.6406,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.96749216,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"95","issue":null,"first_page":"102821","last_page":"102821"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.07320000231266022,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12127","display_name":"Software System Performance and Reliability","score":0.07320000231266022,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.0575999990105629,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11052","display_name":"Energy Load and Power Forecasting","score":0.053300000727176666,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7386000156402588},{"id":"https://openalex.org/keywords/geomagnetic-storm","display_name":"Geomagnetic storm","score":0.635200023651123},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5472000241279602},{"id":"https://openalex.org/keywords/symbolic-regression","display_name":"Symbolic regression","score":0.5077999830245972},{"id":"https://openalex.org/keywords/space-weather","display_name":"Space weather","score":0.48559999465942383},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.48100000619888306},{"id":"https://openalex.org/keywords/storm","display_name":"Storm","score":0.44029998779296875},{"id":"https://openalex.org/keywords/empirical-modelling","display_name":"Empirical modelling","score":0.37720000743865967},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.3547999858856201}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7386000156402588},{"id":"https://openalex.org/C170641098","wikidata":"https://www.wikidata.org/wiki/Q130011","display_name":"Geomagnetic storm","level":4,"score":0.635200023651123},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6251000165939331},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.589900016784668},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5648000240325928},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5472000241279602},{"id":"https://openalex.org/C2776400721","wikidata":"https://www.wikidata.org/wiki/Q18171762","display_name":"Symbolic regression","level":3,"score":0.5077999830245972},{"id":"https://openalex.org/C151325931","wikidata":"https://www.wikidata.org/wiki/Q584093","display_name":"Space weather","level":2,"score":0.48559999465942383},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.48100000619888306},{"id":"https://openalex.org/C105306849","wikidata":"https://www.wikidata.org/wiki/Q81054","display_name":"Storm","level":2,"score":0.44029998779296875},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.34850001335144043},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C108411613","wikidata":"https://www.wikidata.org/wiki/Q79833","display_name":"Solar wind","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2847999930381775},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.275299996137619},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.jocs.2026.102821","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.jocs.2026.102821","pdf_url":null,"source":{"id":"https://openalex.org/S192071280","display_name":"Journal of Computational Science","issn_l":"1877-7503","issn":["1877-7503","1877-7511"],"is_oa":false,"is_in_doaj":false,"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":"Journal of Computational Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.jocs.2026.102821","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.jocs.2026.102821","pdf_url":null,"source":{"id":"https://openalex.org/S192071280","display_name":"Journal of Computational Science","issn_l":"1877-7503","issn":["1877-7503","1877-7511"],"is_oa":false,"is_in_doaj":false,"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":"Journal of Computational Science","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.8844197392463684}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1641233865","https://openalex.org/W1831243570","https://openalex.org/W1934436085","https://openalex.org/W1970488982","https://openalex.org/W1971109208","https://openalex.org/W1998983070","https://openalex.org/W2045371352","https://openalex.org/W2062278296","https://openalex.org/W2075287085","https://openalex.org/W2112615418","https://openalex.org/W2146340143","https://openalex.org/W2472249329","https://openalex.org/W2619262258","https://openalex.org/W2729887693","https://openalex.org/W2765415009","https://openalex.org/W2811318726","https://openalex.org/W2905041712","https://openalex.org/W3016401366","https://openalex.org/W3028472509","https://openalex.org/W4389116447","https://openalex.org/W4406760184","https://openalex.org/W4409250559","https://openalex.org/W4413392264"],"related_works":[],"abstract_inverted_index":{"The":[0,91,149,179],"Disturbance":[1],"Storm":[2],"Time":[3],"(Dst)":[4],"index":[5],"quantifies":[6],"geomagnetic":[7],"storm":[8,154,169,175],"intensity":[9],"by":[10,290],"measuring":[11],"global":[12],"magnetic":[13,58],"field":[14],"variations.":[15],"In":[16,113],"this":[17],"study,":[18],"we":[19,115],"apply":[20],"interpretable":[21,236],"machine-learning":[22],"(ML)":[23],"techniques":[24],"to":[25,76,86,263,296],"derive":[26,252],"data-driven":[27,180,253,281],"models":[28,130,254,332,345,355],"describing":[29],"the":[30,34,42,65,144,157,161,173,183,192,199,202,234,257,287,313,324,343,348,357,364,369,379,383,387],"temporal":[31,259],"evolution":[32],"of":[33,100,176,219,273,280,389],"Dst":[35,82,111,133,258],"index.":[36],"We":[37,60,126,232,251,276,299,311,340,361],"use":[38,116],"historical":[39,153],"data":[40,134],"from":[41],"NASA":[43],"OMNIWeb":[44],"database,":[45],"including":[46],"solar":[47,88],"wind":[48,89],"density,":[49],"bulk":[50],"velocity,":[51],"convective":[52],"electric":[53],"field,":[54],"dynamic":[55],"pressure,":[56],"and":[57,64,103,107,135,146,172,211,244,307,330,371,386],"pressure.":[59],"employ":[61],"KAN":[62],"networks":[63,270],"symbolic":[66,95,193,242,282,349],"regression":[67,96,194,243,283,350],"framework":[68],"PyOperon":[69],",":[70],"based":[71],"on":[72,152],"an":[73,205,216],"evolutionary":[74],"algorithm,":[75],"identify":[77,277],"closed-form":[78],"expressions":[79,195],"linking":[80],"d":[81,84],"/":[83],"t":[85],"key":[87],"parameters.":[90],"equations":[92,301,315],"obtained":[93,285],"via":[94],"form":[97],"a":[98,117,122,167,220,278],"hierarchy":[99,279],"complexity":[101,293],"levels":[102],"capture":[104],"nonlinear":[105],"dependencies":[106],"threshold":[108],"effects":[109],"in":[110,170,188,224,271,338,356,382],"evolution.":[112],"addition,":[114],"conventional":[118],"MLP":[119],"network":[120],"as":[121,143,241,267,294,323],"reference":[123],"black-box":[124,264,366],"model.":[125],"benchmark":[127],"all":[128],"ML":[129],"against":[131],"observed":[132],"compare":[136,261,312],"their":[137],"performance":[138,150],"with":[139,286,302,316,347],"empirical":[140,320,370],"formulations":[141],"such":[142,240,266,322],"Burton-McPherron\u2013Russell":[145],"O\u2019Brien-McPherron":[147,331],"models.":[148,391],"evaluation":[151],"events":[155],"includes":[156],"2003":[158],"Halloween":[159],"storm,":[160,166],"2015":[162],"St.":[163],"Patrick\u2019s":[164],"Day":[165],"moderate":[168],"2017,":[171],"extreme":[174,376],"May":[177],"2024.":[178],"models,":[181,284,321],"particularly":[182],"MLP,":[184],"demonstrate":[185],"superior":[186],"accuracy":[187],"most":[189],"cases.":[190],"While":[191],"provide":[196],"insight":[197],"into":[198],"underlying":[200],"physics,":[201],"results":[203],"highlight":[204],"intrinsic":[206],"trade-off":[207],"between":[208],"model":[209],"interpretability":[210],"predictive":[212],"accuracy.":[213,274,309,339],"This":[214],"is":[215],"extended":[217],"version":[218],"previous":[221],"work":[222],"presented":[223],"Markidis":[225],"et":[226,327,334],"al.":[227],"(2025)":[228],"[1]":[229],".":[230,298],"\u2022":[231,275,310,360],"design":[233],"state-of-the-art":[235],"machine":[237],"learning":[238],"methodologies,":[239],"Kolmogorov\u2013Arnold":[245],"Networks,":[246],"for":[247,255,375],"space":[248],"weather":[249],"applications.":[250],"predicting":[256],"evolution,":[260],"them":[262],"methods,":[265],"multi-perceptron":[268],"neural":[269],"terms":[272],"Operon":[288,297],"framework,":[289],"varying":[291],"equation":[292],"input":[295],"recover":[300],"progressively":[303],"richer":[304],"physical":[305],"content":[306],"increasing":[308],"discovered":[314],"well-established":[317],"magnetospheric":[318],"physics":[319],"Burton-McPherron-Russell":[325],"(Burton":[326],"al.,":[328,335],"1975)":[329],"(O\u2019Brien":[333],"2000,":[336],"2022),":[337],"show":[341,362],"that":[342,363],"best":[344],"found":[346],"approach":[351,367],"outperform":[352],"these":[353],"established":[354],"cases":[358],"considered.":[359],"Multi-Layer-Perceptron":[365],"outperforms":[368],"derived":[372],"equations,":[373],"especially":[374],"events.":[377],"Highlighting":[378],"complex":[380],"dynamics":[381],"Earth\u2019s":[384],"magnetosphere":[385],"cost":[388],"simplified":[390]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2026-03-03T00:00:00"}