An Interpretable LSTM Network for Solar Flare Prediction

Gautam Varma Datla, Haodi Jiang, Jason T.L. Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep learning models are often considered black box models as their internal workings tend to be opaque to the user. Because of this lack of transparency, it is challenging to understand the reasoning behind the model's predictions. Here, we present an approach to making a solar flare prediction model interpretable. This model, built based on a long short-term memory (LSTM) network with an attention mechanism, aims to predict whether an active region (AR) on the Sun's surface would produce a large flare, namely an M- or X-class flare, within 24 hours. The flare events used in this study are collected from the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information. The crux of our approach is to model data samples in an AR as time series and use the LSTM network to capture the temporal dynamics of the data samples. Each data sample has 22 features including magnetic parameters and flare history parameters. To make the model's predictions accountable and reliable, we leverage post hoc model-agnostic techniques, which help elucidate the factors contributing to the predicted output for an input sequence and provide insights into the model's behavior across multiple sequences within an AR. To our knowledge, this is the first time that interpretability has been added to an LSTM-based flare prediction model.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE 35th International Conference on Tools with Artificial Intelligence, ICTAI 2023
PublisherIEEE Computer Society
Pages526-531
Number of pages6
ISBN (Electronic)9798350342734
DOIs
StatePublished - 2023
Externally publishedYes
Event35th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2023 - Atlanta, United States
Duration: Nov 6 2023Nov 8 2023

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Conference

Conference35th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2023
Country/TerritoryUnited States
CityAtlanta
Period11/6/2311/8/23

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Science Applications

Keywords

  • Interpretable deep learning
  • LSTM
  • Solar flares

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