{"id":"https://openalex.org/W7134939050","doi":"https://doi.org/10.1109/icdmw69685.2025.00232","title":"Nowcasting of Aviation Radiation Using Geospace Environment Properties: A Machine Learning Approach","display_name":"Nowcasting of Aviation Radiation Using Geospace Environment Properties: A Machine Learning Approach","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W7134939050","doi":"https://doi.org/10.1109/icdmw69685.2025.00232"},"language":null,"primary_location":{"id":"doi:10.1109/icdmw69685.2025.00232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125512959","display_name":"Sanjib K C","orcid":null},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sanjib K C","raw_affiliation_strings":["Georgia State University,Department of Physics and Astronomy,Atlanta,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia State University,Department of Physics and Astronomy,Atlanta,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079568245","display_name":"Viacheslav M. Sadykov","orcid":"https://orcid.org/0000-0002-4001-1295"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Viacheslav M Sadykov","raw_affiliation_strings":["Georgia State University,Department of Physics and Astronomy,Atlanta,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia State University,Department of Physics and Astronomy,Atlanta,USA","institution_ids":["https://openalex.org/I181565077"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014142286","display_name":"Dustin Kempton","orcid":"https://orcid.org/0000-0002-1837-8365"},"institutions":[{"id":"https://openalex.org/I181565077","display_name":"Georgia State University","ror":"https://ror.org/03qt6ba18","country_code":"US","type":"education","lineage":["https://openalex.org/I181565077"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dustin Kempton","raw_affiliation_strings":["Georgia State University,Department of computer Science,Atlanta,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia State University,Department of computer Science,Atlanta,USA","institution_ids":["https://openalex.org/I181565077"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I181565077"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81824804,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1931","last_page":"1938"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11489","display_name":"Air Traffic Management and Optimization","score":0.4230000078678131,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11489","display_name":"Air Traffic Management and Optimization","score":0.4230000078678131,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10347","display_name":"Atmospheric aerosols and clouds","score":0.0723000019788742,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T12125","display_name":"Aerospace and Aviation Technology","score":0.06379999965429306,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/nowcasting","display_name":"Nowcasting","score":0.8535000085830688},{"id":"https://openalex.org/keywords/aviation","display_name":"Aviation","score":0.5070000290870667},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.26260000467300415},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.24619999527931213}],"concepts":[{"id":"https://openalex.org/C2781013037","wikidata":"https://www.wikidata.org/wiki/Q1433331","display_name":"Nowcasting","level":2,"score":0.8535000085830688},{"id":"https://openalex.org/C74448152","wikidata":"https://www.wikidata.org/wiki/Q765633","display_name":"Aviation","level":2,"score":0.5070000290870667},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4909000098705292},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4749999940395355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46160000562667847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3806999921798706},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.31940001249313354},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.265500009059906},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.24619999527931213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw69685.2025.00232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.48129764199256897,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1572207203","https://openalex.org/W1620056620","https://openalex.org/W1988075771","https://openalex.org/W2058768995","https://openalex.org/W2101234009","https://openalex.org/W2119862467","https://openalex.org/W2135046866","https://openalex.org/W2161343645","https://openalex.org/W2295598076","https://openalex.org/W2523198753","https://openalex.org/W2553578207","https://openalex.org/W2614190558","https://openalex.org/W2890139758","https://openalex.org/W3113050187","https://openalex.org/W4238782871","https://openalex.org/W4367172921","https://openalex.org/W4388505278","https://openalex.org/W4390034075","https://openalex.org/W4390741547","https://openalex.org/W4395115879","https://openalex.org/W4400762160","https://openalex.org/W4409111277","https://openalex.org/W4412100897","https://openalex.org/W4417009428","https://openalex.org/W7134187478"],"related_works":[],"abstract_inverted_index":{"Radiation":[0],"exposure":[1],"at":[2,72,142],"aviation":[3],"altitudes":[4],"presents":[5],"significant":[6],"health":[7],"risks":[8],"to":[9,12,33,60],"aircrews":[10],"due":[11],"the":[13,35,42,98,110,136,139,152,161],"cumulative":[14],"effects":[15],"of":[16,41,80,138,163],"ionizing":[17],"radiation.":[18],"Physics-based":[19],"models":[20,78],"estimate":[21],"radiation":[22,43,63,92,140],"levels":[23,141],"based":[24],"on":[25],"geophysical":[26],"and":[27,38,84,90,130,156,159],"atmospheric":[28,91,157],"parameters,":[29],"but":[30],"often":[31],"struggle":[32],"capture":[34],"highly":[36],"dynamic":[37],"complex":[39],"nature":[40],"environment,":[44],"limiting":[45],"their":[46],"real-time":[47,62],"predictive":[48],"capabilities.":[49],"To":[50],"address":[51],"this":[52],"challenge,":[53],"we":[54,75],"investigate":[55],"machine":[56],"learning":[57],"(ML)":[58],"methods":[59],"enhance":[61],"nowcasting.":[64],"Leveraging":[65],"newly":[66],"compiled,":[67],"ML-ready":[68],"datasets":[69],"\u2014publicly":[70],"available":[71],"[1]":[73],"\u2014":[74],"train":[76],"supervised":[77],"capable":[79],"capturing":[81],"both":[82],"linear":[83],"non-linear":[85],"relationships":[86,150],"between":[87,151],"Geospace":[88,121],"conditions":[89],"levels.":[93],"Our":[94],"experiments":[95],"demonstrate":[96],"that":[97,119],"XGBoost":[99],"model":[100],"achieves":[101],"approximately":[102],"10":[103],"percent":[104],"improvement":[105],"in":[106],"prediction":[107],"accuracy":[108],"over":[109],"considered":[111],"physics-based":[112],"model.":[113],"Furthermore,":[114],"feature":[115],"importance":[116],"analysis":[117],"reveals":[118],"certain":[120],"properties,":[122,129],"specifically":[123],"solar":[124,127],"polar":[125],"fields,":[126],"wind":[128],"neutron":[131],"monitor":[132],"data,":[133],"are":[134],"impacting":[135],"nowcast":[137],"flight":[143],"altitudes.":[144],"These":[145],"findings":[146],"suggest":[147],"meaningful":[148],"physical":[149],"near-Earth":[153],"space":[154,168],"environment":[155],"radiation,":[158],"highlight":[160],"potential":[162],"ML-based":[164],"approaches":[165],"for":[166],"operational":[167],"weather":[169],"applications.":[170]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-03-12T00:00:00"}