Machine learning regression models for prediction of multiple ionospheric parameters

Iban, Muzaffer Can; Şentürk, Erman

doi:10.1016/j.asr.2021.11.026

Cited by 27 publications

(9 citation statements)

References 53 publications

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“…Machine learning techniques offer a more exploratory approach to data analysis and emphasize the importance of data visualization (Jordan & Mitchell, 2015). Moreover, the feasibility of machine learning models in TEC prediction has been proven (Muzaffer Can Iban et al., 2021). Despite the wide range of applications of machine learning, there are still many undeveloped or underutilized methods in the study of near space environment (Camporeale et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Ionospheric TEC Prediction Based on Ensemble Learning Models

Zhou,

Liu,

et al. 2024

Space Weather

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In this paper, we propose the usage of an ensemble learning approach for predicting total electron content (TEC). The training data set spans from 2007 to 2016, while the testing data set is set to the year 2017. The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30‐day and 90‐day TEC averages. Specifically, eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree were utilized for 1‐hr TEC prediction at high‐ (80°W, 80°N), mid‐ (80°W, 40°N), and low‐ latitudes (80°W, 10°N). Results indicate that all three models performed well in predicting TEC, with a mean error of only approximately 0.6 TECU at high‐ and mid‐ latitudes and 1.13 TECU at low latitudes. At the same time, we compared the model with 1‐day Beijing University of Aeronautics and Astronautics model during the period of magnetic storm from 25 August 2018 to 27 August 2018 and a quiet period from 13 December 2018 to 15 December 2018. In the magnetic storm period, Our model showed an average reduction of 1.83 TECU compared to BUAA model. During the quiet period, XGBoost exhibit an average error that is 1.14 TECU lower than that of BUAA model. Moreover, TEC prediction over the region between the 20°N–45°N and 70°E−120°E during geomagnetic storm has an error of 2.74 TECU, showing the stability and superiority of XGBoost. Overall, the ensemble learning approach exhibits its advantage in predicting TEC.

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Section: Introductionmentioning

confidence: 99%

Ionospheric TEC Prediction Based on Ensemble Learning Models

Zhou,

Liu,

et al. 2024

Space Weather

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“…(2022) constructed a 3D empirical model of electron density based on a China Seismo‐Electromagnetic Satellite for hmF2 and NmF2 prediction. Iban and Şentürk (2022) analyzed the forecasting performance of machine learning models in the foF2, hmF2, and TEC. W. Li et al.…”

Section: Introductionmentioning

confidence: 99%

“…H. Huang et al (2022) constructed a 3D empirical model of electron density based on a China Seismo-Electromagnetic Satellite for hmF2 and NmF2 prediction. Iban and Şentürk (2022) analyzed the forecasting performance of machine learning models in the foF2, hmF2, and TEC. W. Li et al (2020) established a global model for forecasting foF2 and hmF2 using the ANN optimized by genetic algorithm optimization, which can better capture global or regional ionospheric spatiotemporal characteristics.…”

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confidence: 99%

A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer

Shi,

Yang,

Wang

et al. 2023

Space Weather

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To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.

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“…This study proposes a method based on classical machine learning algorithms by optimizing signal processing processes. This study's Gaussian process regression (GPR), regression tree ensembles, and regression trees were preferred because of their high-performance [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

Kandaz

Uçar

Polat

et al. 2022

Computational and Mathematical Methods in Medicine

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Objective. Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman’s feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results. The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion. According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.

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Machine learning regression models for prediction of multiple ionospheric parameters

Cited by 27 publications

References 53 publications

Ionospheric TEC Prediction Based on Ensemble Learning Models

Ionospheric TEC Prediction Based on Ensemble Learning Models

A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection

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