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DTSTAMP:20250626T233530Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241121T100000
DTEND;TZID=America/New_York:20241121T170000
UID:submissions.supercomputing.org_SC24_sess534_post254@linklings.com
SUMMARY:Meteorologic Real-Time Extreme Learning Machine for Pressure Predi
 ction
DESCRIPTION:Anagha Ram (University of Chicago); Kate Keahey (University of
  Chicago, Argonne National Laboratory (ANL)); and Alicia Esquivel Morel (U
 niversity of Missouri, Columbia)\n\nSignificant advances in weather predic
 tion stemmed primarily from combining observational data, sophisticated mo
 deling techniques, and analysis of simulated or historical weather data. A
 dditionally, the applicability of machine learning-based applications on e
 dge devices has expanded, addressing a variety of use cases, including wea
 ther prediction. This study builds on prior research using an Extreme Lear
 ning Machine (ELM) approach to detect weather anomalies in real-time, enha
 ncing existing predictive systems. Our model is implemented on IBIS, an ad
 aptable edge computing framework for multi-sensor data collection. The ELM
  model, applied to detect real-time weather anomalies, offers fast trainin
 g and operational efficiency. Data on atmospheric phenomena, including pre
 ssure and wind, was generated and stored in a time series database. The mo
 del was then trained on 80% of 550,000 records. Our experiments demonstrat
 ed a 92% R2_score, supporting its effectiveness. Our work within IBIS repr
 esents a cost-effective and scalable solution for collecting, monitoring, 
 and predicting hazardous atmospheric conditions.\n\nRegistration Category:
  Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afzal 
 (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen National Hig
 h Performance Computing Center); Sally Ellingson (University of Kentucky);
  and Alan Sussman (University of Maryland)\n\n
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