Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning
Predicting the behaviour of the Earth’s ionosphere is crucial for the ground-based and spaceborne technologies that rely on it. This paper presents a novel way of inferring ionospheric electron density profiles and electron temperature profiles using machine learning. The analysis is based on the Ne...
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MDPI AG
2025-06-01
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| author | Jean de Dieu Nibigira Richard Marchand |
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| description | Predicting the behaviour of the Earth’s ionosphere is crucial for the ground-based and spaceborne technologies that rely on it. This paper presents a novel way of inferring ionospheric electron density profiles and electron temperature profiles using machine learning. The analysis is based on the Nearest Neighbour (NNB) and Radial Basis Function (RBF) regression models. Synthetic data sets used to train and validate these two inference models are constructed using the International Reference Ionosphere (IRI 2020) model with randomly chosen years (1987–2022), months (1–12), days (1–31), latitudes (−60 to 60°), longitudes (0, 360°), and times (0–23 h), at altitudes ranging from 95 to 600 km. The NNB and RBF models use the constructed ionosonde-like profiles to infer complete ISR-like profiles. The results show that the inference of ionospheric electron density profiles is better with the NNB model than with the RBF model, while the RBF model is better at inferring the electron temperature profiles. A major and unexpected finding of this research is the ability of the two models to infer full electron temperature profiles that are not provided by ionosondes using the same truncated electron density data set used to infer electron density profiles. NNB and RBF models generally over- or underestimate the inferred electron density and electron temperature values, especially at higher altitudes, but they tend to produce good matches at lower altitudes. Additionally, maximum absolute relative errors for electron density and temperature inferences are found at higher altitudes for both NNB and RBF models. |
| format | Article |
| id | doaj-art-022e448f32684fb0887517c7d11d8d28 |
| institution | DOAJ |
| issn | 2571-6182 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-022e448f32684fb0887517c7d11d8d282025-08-20T03:16:36ZengMDPI AGPlasma2571-61822025-06-01822410.3390/plasma8020024Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine LearningJean de Dieu Nibigira0Richard Marchand1Department of Physics, University of Alberta, Edmonton, AB T6G 2R3, CanadaDepartment of Physics, University of Alberta, Edmonton, AB T6G 2R3, CanadaPredicting the behaviour of the Earth’s ionosphere is crucial for the ground-based and spaceborne technologies that rely on it. This paper presents a novel way of inferring ionospheric electron density profiles and electron temperature profiles using machine learning. The analysis is based on the Nearest Neighbour (NNB) and Radial Basis Function (RBF) regression models. Synthetic data sets used to train and validate these two inference models are constructed using the International Reference Ionosphere (IRI 2020) model with randomly chosen years (1987–2022), months (1–12), days (1–31), latitudes (−60 to 60°), longitudes (0, 360°), and times (0–23 h), at altitudes ranging from 95 to 600 km. The NNB and RBF models use the constructed ionosonde-like profiles to infer complete ISR-like profiles. The results show that the inference of ionospheric electron density profiles is better with the NNB model than with the RBF model, while the RBF model is better at inferring the electron temperature profiles. A major and unexpected finding of this research is the ability of the two models to infer full electron temperature profiles that are not provided by ionosondes using the same truncated electron density data set used to infer electron density profiles. NNB and RBF models generally over- or underestimate the inferred electron density and electron temperature values, especially at higher altitudes, but they tend to produce good matches at lower altitudes. Additionally, maximum absolute relative errors for electron density and temperature inferences are found at higher altitudes for both NNB and RBF models.https://www.mdpi.com/2571-6182/8/2/24ionosphereelectron density and temperature profilesmachine learningregression modelsionosonde |
| spellingShingle | Jean de Dieu Nibigira Richard Marchand Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning Plasma ionosphere electron density and temperature profiles machine learning regression models ionosonde |
| title | Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning |
| title_full | Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning |
| title_fullStr | Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning |
| title_full_unstemmed | Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning |
| title_short | Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning |
| title_sort | ionospheric electron density and temperature profiles using ionosonde like data and machine learning |
| topic | ionosphere electron density and temperature profiles machine learning regression models ionosonde |
| url | https://www.mdpi.com/2571-6182/8/2/24 |
| work_keys_str_mv | AT jeandedieunibigira ionosphericelectrondensityandtemperatureprofilesusingionosondelikedataandmachinelearning AT richardmarchand ionosphericelectrondensityandtemperatureprofilesusingionosondelikedataandmachinelearning |