A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction

03/11/2022
by   Yuchen Dang, et al.
0

Solar activity has significant impacts on human activities and health. One most commonly used measure of solar activity is the sunspot number. This paper compares three important non-deep learning models, four popular deep learning models, and their five ensemble models in forecasting sunspot numbers. Our proposed ensemble model XGBoost-DL, which uses XGBoost as a two-level nonlinear ensemble method to combine the deep learning models, achieves the best forecasting performance among all considered models and the NASA's forecast. Our XGBoost-DL forecasts a peak sunspot number of 133.47 in May 2025 for Solar Cycle 25 and 164.62 in November 2035 for Solar Cycle 26, similar to but later than the NASA's at 137.7 in October 2024 and 161.2 in December 2034.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/27/2017

Random Forest Ensemble of Support Vector Regression Models for Solar Power Forecasting

To mitigate the uncertainty of variable renewable resources, two off-the...
research
05/17/2020

Forecasting Solar Activity with Two Computational Intelligence Models (A Comparative Study)

Solar activity It is vital to accurately predict solar activity, in orde...
research
09/12/2022

Operational solar flare forecasting via video-based deep learning

Operational flare forecasting aims at providing predictions that can be ...
research
12/28/2020

Shape-based Feature Engineering for Solar Flare Prediction

Solar flares are caused by magnetic eruptions in active regions (ARs) on...
research
08/20/2023

Homogenising SoHO/EIT and SDO/AIA 171Å Images: A Deep Learning Approach

Extreme Ultraviolet images of the Sun are becoming an integral part of s...
research
08/29/2023

Probabilistic solar flare forecasting using historical magnetogram data

Solar flare forecasting research using machine learning (ML) has focused...
research
02/21/2020

Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization

Ensemble smoother (ES) has been widely used in various research fields t...

Please sign up or login with your details

Forgot password? Click here to reset