Presentation

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Meteorologic Real-Time Extreme Learning Machine for Pressure Prediction

SessionResearch, ACM SRC, and Doctoral Showcase Posters

DescriptionSignificant advances in weather prediction stemmed primarily from combining observational data, sophisticated modeling techniques, and analysis of simulated or historical weather data. Additionally, the applicability of machine learning-based applications on edge devices has expanded, addressing a variety of use cases, including weather prediction. This study builds on prior research using an Extreme Learning Machine (ELM) approach to detect weather anomalies in real-time, enhancing existing predictive systems. Our model is implemented on IBIS, an adaptable edge computing framework for multi-sensor data collection. The ELM model, applied to detect real-time weather anomalies, offers fast training and operational efficiency. Data on atmospheric phenomena, including pressure and wind, was generated and stored in a time series database. The model was then trained on 80% of 550,000 records. Our experiments demonstrated a 92% R2_score, supporting its effectiveness. Our work within IBIS represents a cost-effective and scalable solution for collecting, monitoring, and predicting hazardous atmospheric conditions.

Authors

Anagha Ram

University of Chicago

Kate Keahey

University of Chicago

Argonne National Laboratory (ANL)