A Machine Learning Approach for Predicting the Sunspot of Solar Cycle

Khan, Thaharim; Arafat, Faisal; Mojumdar, Mayen Uddin; Rajbongshi, Aditya; Siddiquee, Shah Md. Tanvir; Chakraborty, Narayan Ranjan

doi:10.1109/icccnt49239.2020.9225427

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Panigrahi et al [27] integrates two statistical models with SVM. Hybrid PSO with extreme learning machine (ELM), with a feature extraction is presented in[28] Khan et al [29] combined ANN with LSTM. Benson et al proposed a hybrid of two DL models: LSTM and WaveNet [30].…”

Section: Related Workmentioning

confidence: 99%

Novel residual hybrid machine learning for solar activity prediction in smart cities

Abdulkadir,

Hasan,

Islam

et al. 2023

Earth Sci Inform

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Predicting global solar activity is crucial for smart cities, especially space activities, communication industries, and climate change monitoring. The recently developed models to predict solar activity based on stand-alone arti cial intelligence, based on machine and deep learning models, and hybrid models are promising. Yet they may not be effective at capturing simpler linear patterns in the data and often fail to provide reliable predictions due to the computationally expensive and complex. This article proposed a novel residual hybrid machine learning method integrating linear regression machine learning, and deep learning neural networks for solving predictive accuracy in individual machine learning models that reduces complexity. The residual hybrid model leverages the capacities of the support vector machine (SVM) and long short-term memory neural network (LSTM) for hybrid SVM-LSTM model. The performance of the model is evaluated using the correlation coe cient, determination coe cient, rootmean-squared error (RMSE) and mean-absolute error. The simulation results indicated that compared to the SVM-LSTM, the training and testing RMSE of the LSTM is reduced by 76.62% and 71.18%, respectively. It also decreases the training and testing RMSE of the SVM by 77.06% and 71.81%, respectively. The proposed model can be implemented as reliable solution for accurately predicting solar activities in smart cities.

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Section: Related Workmentioning

confidence: 99%

Novel residual hybrid machine learning for solar activity prediction in smart cities

Abdulkadir,

Hasan,

Islam

et al. 2023

Earth Sci Inform

View full text Add to dashboard Cite

show abstract

“…[14] proposed to use recursive Levenberg-Marquardt Bayesian in RNN to forecast electricity spot prices as well as compute the uncertainty of the model. Other researchers used CNN to predict wind power [15], LSTM to predict wind speed [16], weather [8], [9], sunspot [10], [17], [18], or combine CNN and LSTM [19], [20], RNN and LSTM [21] to forecast the output in time series datasets. Recently, In 2021, Zhou proposed a novel approach called Informer to deal with heavy memory when using long input sequences [22].…”

Section: Related Workmentioning

confidence: 99%

Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction

Nghiem

et al. 2022

2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)

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Convolutional neural networks (CNN) and Long short-term memory (LSTM) provide stateof-the-art performance in various tasks. However, these models are faced with overfitting on small data and cannot measure uncertainty, which have a negative effect on their generalization abilities. In addition, the prediction task can face many challenges because of the complex long-term fluctuations, especially in time series datasets. Recently, applying Bayesian inference in deep learning to estimate the uncertainty in the model prediction was introduced. This approach can be highly robust to overfitting and allows to estimate uncertainty. In this paper, we propose a novel approach using Bayesian inference in a hybrid CNN-LSTM model called CNN-Bayes LSTM for time series prediction. The experiments have been conducted on two real time series datasets, namely sunspot and weather datasets. The experimental results show that the proposed CNN-Bayes LSTM model is more effective than other forecasting models in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as well as for uncertainty quantification.

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A Review of Machine Learning Methods Applied in Sunspot Prediction

Xiao

2021

2021 International Conference on Networking, Communications and Information Technology (NetCIT)

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A Machine Learning Approach for Predicting the Sunspot of Solar Cycle

Cited by 4 publications

References 4 publications

Novel residual hybrid machine learning for solar activity prediction in smart cities

Novel residual hybrid machine learning for solar activity prediction in smart cities

Applying Bayesian inference in a hybrid CNN-LSTM model for time-series prediction

A Review of Machine Learning Methods Applied in Sunspot Prediction

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