49 research outputs found
Heuristic algorithm for interpretation of multi-valued attributes in similarity-based fuzzy relational databases
AbstractIn this work, we are presenting implementation details and extended scalability tests of the heuristic algorithm, which we had used in the past [1,2] to discover knowledge from multi-valued data entries stored in similarity-based fuzzy relational databases. The multi-valued symbolic descriptors, characterizing individual attributes of database records, are commonly used in similarity-based fuzzy databases to reflect uncertainty about the recorded observation. In this paper, we present an algorithm, which we developed to precisely interpret such non-atomic values and to transfer the fuzzy database tuples to the forms acceptable for many regular (i.e. atomic values based) data mining algorithms
Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions
This study aims to evaluate the performance of deep learning models in
predicting M-class solar flares with a prediction window of 24 hours,
using hourly sampled full-disk line-of-sight (LoS) magnetogram images,
particularly focusing on the often overlooked flare events corresponding to the
near-limb regions (beyond 70 of the solar disk). We trained
three well-known deep learning architectures--AlexNet, VGG16, and ResNet34
using transfer learning and compared and evaluated the overall performance of
our models using true skill statistics (TSS) and Heidke skill score (HSS) and
computed recall scores to understand the prediction sensitivity in central and
near-limb regions for both X- and M-class flares. The following points
summarize the key findings of our study: (1) The highest overall performance
was observed with the AlexNet-based model, which achieved an average
TSS0.53 and HSS0.37; (2) Further, a spatial analysis of recall
scores disclosed that for the near-limb events, the VGG16- and ResNet34-based
models exhibited superior prediction sensitivity. The best results, however,
were seen with the ResNet34-based model for the near-limb flares, where the
average recall was approximately 0.59 (the recall for X- and M-class was 0.81
and 0.56 respectively) and (3) Our research findings demonstrate that our
models are capable of discerning complex spatial patterns from full-disk
magnetograms and exhibit skill in predicting solar flares, even in the vicinity
of near-limb regions. This ability holds substantial importance for operational
flare forecasting systems.Comment: This is a preprint accepted at the 22nd International Conference on
Machine Learning and Applications (ICMLA), 2023. 7 Pages, 6 Figure
Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods
This paper contributes to the growing body of research on deep learning
methods for solar flare prediction, primarily focusing on highly overlooked
near-limb flares and utilizing the attribution methods to provide a post hoc
qualitative explanation of the model's predictions. We present a solar flare
prediction model, which is trained using hourly full-disk line-of-sight
magnetogram images and employs a binary prediction mode to forecast
M-class flares that may occur within the following 24-hour period. To
address the class imbalance, we employ a fusion of data augmentation and class
weighting techniques; and evaluate the overall performance of our model using
the true skill statistic (TSS) and Heidke skill score (HSS). Moreover, we
applied three attribution methods, namely Guided Gradient-weighted Class
Activation Mapping, Integrated Gradients, and Deep Shapley Additive
Explanations, to interpret and cross-validate our model's predictions with the
explanations. Our analysis revealed that full-disk prediction of solar flares
aligns with characteristics related to active regions (ARs). In particular, the
key findings of this study are: (1) our deep learning models achieved an
average TSS=0.51 and HSS=0.35, and the results further demonstrate a competent
capability to predict near-limb solar flares and (2) the qualitative analysis
of the model explanation indicates that our model identifies and uses features
associated with ARs in central and near-limb locations from full-disk
magnetograms to make corresponding predictions. In other words, our models
learn the shape and texture-based characteristics of flaring ARs even at
near-limb areas, which is a novel and critical capability with significant
implications for operational forecasting.Comment: 19 pages, 7 figures, Preprint accepted at European Conference on
Machine Learning and Principles and Practice of Knowledge Discovery in
Databases (ECML-PKDD) 202
Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks
Solar flare prediction is a central problem in space weather forecasting and
recent developments in machine learning and deep learning accelerated the
adoption of complex models for data-driven solar flare forecasting. In this
work, we developed an attention-based deep learning model as an improvement
over the standard convolutional neural network (CNN) pipeline to perform
full-disk binary flare predictions for the occurrence of M1.0-class
flares within the next 24 hours. For this task, we collected compressed images
created from full-disk line-of-sight (LoS) magnetograms. We used data-augmented
oversampling to address the class imbalance issue and used true skill statistic
(TSS) and Heidke skill score (HSS) as the evaluation metrics. Furthermore, we
interpreted our model by overlaying attention maps on input magnetograms and
visualized the important regions focused on by the model that led to the
eventual decision. The significant findings of this study are: (i) We
successfully implemented an attention-based full-disk flare predictor ready for
operational forecasting where the candidate model achieves an average
TSS=0.540.03 and HSS=0.370.07. (ii) we demonstrated that our
full-disk model can learn conspicuous features corresponding to active regions
from full-disk magnetogram images, and (iii) our experimental evaluation
suggests that our model can predict near-limb flares with adept skill and the
predictions are based on relevant active regions (ARs) or AR characteristics
from full-disk magnetograms.Comment: This is a preprint accepted at the 6th International Conference on
Artificial Intelligence and Knowledge Engineering (AIKE), 2023. 8 pages, 6
figure
Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting
This paper presents a post hoc analysis of a deep learning-based full-disk
solar flare prediction model. We used hourly full-disk line-of-sight
magnetogram images and selected binary prediction mode to predict the
occurrence of M1.0-class flares within 24 hours. We leveraged custom data
augmentation and sample weighting to counter the inherent class-imbalance
problem and used true skill statistic and Heidke skill score as evaluation
metrics. Recent advancements in gradient-based attention methods allow us to
interpret models by sending gradient signals to assign the burden of the
decision on the input features. We interpret our model using three post hoc
attention methods: (i) Guided Gradient-weighted Class Activation Mapping, (ii)
Deep Shapley Additive Explanations, and (iii) Integrated Gradients. Our
analysis shows that full-disk predictions of solar flares align with
characteristics related to the active regions. The key findings of this study
are: (1) We demonstrate that our full disk model can tangibly locate and
predict near-limb solar flares, which is a critical feature for operational
flare forecasting, (2) Our candidate model achieves an average
TSS=0.510.05 and HSS=0.380.08, and (3) Our evaluation suggests that
these models can learn conspicuous features corresponding to active regions
from full-disk magnetograms.Comment: 15 pages, 5 figures. This is a preprint accepted at the 26th
International Conference on Discovery Science (DS2023). arXiv admin note:
text overlap with arXiv:2307.1587
Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting
Solar flare prediction is a central problem in space weather forecasting and
has captivated the attention of a wide spectrum of researchers due to recent
advances in both remote sensing as well as machine learning and deep learning
approaches. The experimental findings based on both machine and deep learning
models reveal significant performance improvements for task specific datasets.
Along with building models, the practice of deploying such models to production
environments under operational settings is a more complex and often
time-consuming process which is often not addressed directly in research
settings. We present a set of new heuristic approaches to train and deploy an
operational solar flare prediction system for M1.0-class flares with two
prediction modes: full-disk and active region-based. In full-disk mode,
predictions are performed on full-disk line-of-sight magnetograms using deep
learning models whereas in active region-based models, predictions are issued
for each active region individually using multivariate time series data
instances. The outputs from individual active region forecasts and full-disk
predictors are combined to a final full-disk prediction result with a
meta-model. We utilized an equal weighted average ensemble of two base
learners' flare probabilities as our baseline meta learner and improved the
capabilities of our two base learners by training a logistic regression model.
The major findings of this study are: (i) We successfully coupled two
heterogeneous flare prediction models trained with different datasets and model
architecture to predict a full-disk flare probability for next 24 hours, (ii)
Our proposed ensembling model, i.e., logistic regression, improves on the
predictive performance of two base learners and the baseline meta learner
measured in terms of two widely used metrics True Skill Statistic (TSS) and
Heidke Skill core (HSS), and (iii) Our result analysis suggests that the
logistic regression-based ensemble (Meta-FP) improves on the full-disk model
(base learner) by in terms TSS and in terms of HSS.
Similarly, it improves on the AR-based model (base learner) by and
in terms of TSS and HSS respectively. Finally, when compared to the
baseline meta model, it improves on TSS by and HSS by