Regional improvement of IRI extracted ionospheric electron density by compactly supported base functions using GPS observations

Amerian, Yazdan; Hossainali, Masoud Mashhadi; Voosoghi, Behzad

doi:10.1016/j.jastp.2012.09.011

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…The complex nature of the global ionosphere is shaped by solar and interplanetary activities as wells inputs from stratosphere, troposphere and mesosphere. Various long and short term physics and data assimilation based forecasts have been developed in the past (Amerian et al., 2013; Jakowski et al., 2011). However, physics based models hardly capture the complex structure of the ionosphere as the mathematical relationship between the solar, geomagnetic and lower atmospheric parameters across the various ionospheric geographic regions and different altitudes is not comprehensively and precisely known.…”

Section: Summary and Discussionmentioning

confidence: 99%

Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods

et al. 2021

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Section: Summary and Discussionmentioning

confidence: 99%

Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods

et al. 2021

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“…The ionosphere is the uppermost atmospheric layer comprising a combination of ionized gas, ion, and electron that coat the earth at the height of 60 to 2000 km from the surface of the earth [36]. Ionosphere acts as a protector of the earth and life on it from any threat of space weather phenomena.…”

Section: Datasetsmentioning

confidence: 99%

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

Salleh

Yuhaniz

Sabri

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in the equatorial and low-latitude regions due to the lack of observation data contributed by these areas during the development of the empirical model. The machine learning technique is an alternative method used to develop the predictive model. Thus, in this study, the machine learning techniques that can be applied are investigated. The aim is to improve the predictive model in terms of reducing the total electron content deviation, increasing the accuracy and minimizing the error. In this review, the techniques used in previous works will be compared. The artificial neural network is found to be a suitable technique and the most favorable from the review conducted. Also, this technique can provide an accurate model for time series data and fewer errors compared to other techniques. However, due to the size and complexity of the data, the deep neural network technique that is an improved artificial neural network technique is suggested. By using this technique, an accurate ionosphere predictive model in equatorial and low region area is expected. In the future, this study will analyze further by using computing tools and real-time data.

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“…We present a new method based on the non-identical degrees of the B-spline scaling function to overcome the disadvantages of the voxel-based and polynomial function-based methods. The B-spline function has already been used in ionosphere tomography and has shown high ability in ionosphere modeling (Amerian et al 2013a, b). Here, the B-spline scaling function with different degrees and resolution levels is used in the tropospheric tomography.…”

Section: Introductionmentioning

confidence: 99%

B-spline function-based approach for GPS tropospheric tomography

2020

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Regional improvement of IRI extracted ionospheric electron density by compactly supported base functions using GPS observations

Cited by 16 publications

References 17 publications

Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods

Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

B-spline function-based approach for GPS tropospheric tomography

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