scholarly journals Support Vector Machine for Regional Ionospheric Delay Modeling

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2947 ◽  
Author(s):  
Zhengxie Zhang ◽  
Shuguo Pan ◽  
Chengfa Gao ◽  
Tao Zhao ◽  
Wang Gao
Keyword(s):  
Support Vector Machine ◽  
Regression Model ◽  
Dimensional Space ◽  
Back Propagation ◽  
Single Frequency ◽  
Support Vector ◽  
Electron Content ◽  
Total Electron ◽  
Ionosphere Modeling ◽  
Mean Square Errors

The distribution of total electron content (TEC) in the ionosphere is irregular and complex, and it is hard to model accurately. The polynomial (POLY) model is used extensively for regional ionosphere modeling in two-dimensional space. However, in the active period of the ionosphere, the POLY model is difficult to reflect the distribution and variation of TEC. Aiming at the limitation of the regional POLY model, this paper proposes a new ionosphere modeling method with combining the support vector machine (SVM) regression model and the POLY model. Firstly, the POLY model is established using observations of regional continuously operating reference stations (CORS). Then the SVM regression model is trained to compensate the model error of POLY, and the TEC SVM-P model is obtained by the combination of the POLY and the SVM. The fitting accuracies of the models are verified with the root mean square errors (RMSEs) and static single-frequency precise point positioning (PPP) experiments. The results show that the RMSE of the SVM-P is 0.980 TECU (TEC unit), which produces an improvement of 17.3% compared with the POLY model (1.185 TECU). Using SVM-P models, the positioning accuracies of single-frequency PPP are improved over 40% compared with those using POLY models. The SVM-P is also compared with the back-propagation neural network combined with POLY (BPNN-P), and its performance is also better than BPNN-P (1.070 TECU).

scholarly journals LEAST SQUARE SUPPORT VECTOR MACHINE FOR DETECTION OF TECSEISMO- IONOSPHERIC ANOMALIES ASSOCIATED WITH THE POWERFUL NEPAL EARTHQUAKE (M<sub>w</sub> = 7.5) OF 25 APRIL 2015

2016 ◽  
Vol III-8 ◽  
pp. 3-11
Author(s):  
M. Akhoondzadeh
Keyword(s):  
Support Vector Machine ◽  
Main Shock ◽  
Warning System ◽  
Least Square ◽  
Support Vector ◽  
Electron Content ◽  
Total Electron ◽  
Strong Earthquakes ◽  
Nepal Earthquake ◽  
New Challenges

Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth&thinsp;=&thinsp;15.0 km) a powerful earthquake of M&lt;sub&gt;w&lt;/sub&gt;&thinsp;=&thinsp;7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection.

scholarly journals Extending the coverage area of regional ionosphere maps using a support vector machine algorithm

2019 ◽  
Vol 37 (1) ◽  
pp. 77-87 ◽  
Author(s):  
Mingyu Kim ◽  
Jeongrae Kim
Keyword(s):  
Support Vector Machine ◽  
Environmental Parameters ◽  
Substantial Improvement ◽  
Ionospheric Delay ◽  
Support Vector ◽  
Electron Content ◽  
Accuracy Evaluation ◽  
Coverage Area ◽  
Total Electron ◽  
Map Data

Abstract. The coverage of regional ionosphere maps is determined by the distribution of ground-based monitoring stations, e.g., GNSS receivers. Since ionospheric delay has a high spatial correlation, ionosphere map coverage can be extended using spatial extrapolation methods. This paper proposes a support vector machine (SVM) to extrapolate the ionosphere map data with solar and geomagnetic parameters. One year of IGS ionospheric delay map data over South Korea is used to train the SVM algorithm. Subsequently, 1 month of ionospheric delay data outside the input data region is estimated. In addition to solar and geomagnetic environmental parameters, the ionospheric delay data from the inner data region are used to estimate the ionospheric delay data for the outside region. The accuracy evaluation is performed at three levels of range −5, 10, and 15∘ outside the inner data regions. The extrapolation errors are 0.33 TECU (total electron content unit) for the 5∘ region and 1.95 TECU for the 15∘ region. These values are substantially lower than the GPS Klobuchar model error values. Comparison with another machine learning extrapolation method, the neural network, shows a substantial improvement of up to 26.7 %.

scholarly journals LEAST SQUARE SUPPORT VECTOR MACHINE FOR DETECTION OF TECSEISMO- IONOSPHERIC ANOMALIES ASSOCIATED WITH THE POWERFUL NEPAL EARTHQUAKE (Mw = 7.5) OF 25 APRIL 2015

2016 ◽  
Vol III-8 ◽  
pp. 3-11
Author(s):  
M. Akhoondzadeh
Keyword(s):  
Support Vector Machine ◽  
Main Shock ◽  
Warning System ◽  
Least Square ◽  
Support Vector ◽  
Electron Content ◽  
Total Electron ◽  
Strong Earthquakes ◽  
Nepal Earthquake ◽  
New Challenges

Due to the irrepalable devastations of strong earthquakes, accurate anomaly detection in time series of different precursors for creating a trustworthy early warning system has brought new challenges. In this paper the predictability of Least Square Support Vector Machine (LSSVM) has been investigated by forecasting the GPS-TEC (Total Electron Content) variations around the time and location of Nepal earthquake. In 77 km NW of Kathmandu in Nepal (28.147° N, 84.708° E, depth&thinsp;=&thinsp;15.0 km) a powerful earthquake of M<sub>w</sub>&thinsp;=&thinsp;7.8 took place at 06:11:26 UTC on April 25, 2015. For comparing purpose, other two methods including Median and ANN (Artificial Neural Network) have been implemented. All implemented algorithms indicate on striking TEC anomalies 2 days prior to the main shock. Results reveal that LSSVM method is promising for TEC sesimo-ionospheric anomalies detection.

scholarly journals Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1004
Author(s):  
Song Li ◽  
Tianhe Xu ◽  
Nan Jiang ◽  
Honglei Yang ◽  
Shuaimin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Efficiency ◽  
Rapid Development ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Improvement Rate ◽  
Zenith Tropospheric Delay ◽  
Background Data ◽  
Mean Square Errors

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

scholarly journals Statistical study of medium-scale traveling ionospheric disturbances in low-latitude ionosphere using an automatic algorithm

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Pin-Hsuan Cheng ◽  
Charles Lin ◽  
Yuichi Otsuka ◽  
Hanli Liu ◽  
Panthalingal Krishanunni Rajesh ◽  
...  
Keyword(s):  
Climate Model ◽  
Geomagnetic Latitude ◽  
Detection Algorithm ◽  
Support Vector ◽  
Electron Content ◽  
Ionospheric Disturbances ◽  
Low Latitude ◽  
Total Electron ◽  
Medium Scale ◽  
Traveling Ionospheric Disturbances

AbstractThis study investigates the medium-scale traveling ionospheric disturbances (MSTIDs) statistically at the low-latitude equatorial ionization anomaly (EIA) region in the northern hemisphere. We apply the automatic detection algorithm including the three-dimensional fast Fourier transform (3-D FFT) and support vector machine (SVM) on total electron content (TEC) observations, derived from a network of ground-based global navigation satellite system (GNSS) receivers in Taiwan (14.5° N geomagnetic latitude; 32.5° inclination), to identify MSTID from other waves or irregularity features. The obtained results are analyzed statistically to examine the behavior of low-latitude MSTIDs. Statistical results indicate the following characteristics. First, the southward (equatorward) MSTIDs are observed almost every day during 0800–2100 LT in Spring and Winter. At midnight, southward MSTIDs are more discernible in Summer and majority of them are propagating from Japan to Taiwan. Second, northward (poleward) MSTIDs are more frequently detected during 1200–2100 LT in Spring and Summer with the secondary peak of occurrence between day of year (DOY) 100–140 during 0000–0300 LT. The characteristics of the MSTIDs are interpreted with additional observations from radio occultation (RO) soundings of FORMOSAT-3/COSMIC as well as modeled atmospheric waves from the high-resolution Whole Atmosphere Community Climate Model (WACCM) suggesting that the nighttime MSTIDs in Summer is likely connected to the atmospheric gravity waves (AGWs).

scholarly journals Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias

2021 ◽  
Vol 7 ◽  
Author(s):  
Juan Durazo ◽  
Eric J. Kostelich ◽  
Alex Mahalov
Keyword(s):  
Data Assimilation ◽  
Space Weather ◽  
Bias Correction ◽  
Geomagnetic Storms ◽  
Circulation Model ◽  
Storm Event ◽  
Electron Content ◽  
Systematic Bias ◽  
Total Electron ◽  
Mean Square Errors

The dynamics of many models of physical systems depend on the choices of key parameters. This paper describes the results of some observing system simulation experiments using a first-principles model of the Earth’s ionosphere, the Thermosphere Ionosphere Electrodynamics Global Circulation Model (TIEGCM), which is driven by parameters that describe solar activity, geomagnetic conditions, and the state of the thermosphere. Of particular interest is the response of the ionosphere (and predictions of space weather generally) during geomagnetic storms. Errors in the overall specification of driving parameters for the TIEGCM (and similar dynamical models) may be especially large during geomagnetic storms, because they represent significant perturbations away from more typical interactions of the earth-sun system. Such errors can induce systematic biases in model predictions of the ionospheric state and pose difficulties for data assimilation methods, which attempt to infer the model state vector from a collection of sparse and/or noisy measurements. Typical data assimilation schemes assume that the model produces an unbiased estimate of the truth. This paper tests one potential approach to handle the case where there is some systematic bias in the model outputs. Our focus is on the TIEGCM when it is driven with solar and magnetospheric inputs that are systematically misspecified. We report results from observing system experiments in which synthetic electron density vertical profiles are generated at locations representative of the operational FormoSat-3/COSMIC satellite observing platforms during a moderate (G2, Kp = 6) geomagnetic storm event on September 26–27, 2011. The synthetic data are assimilated into the TIEGCM using the Local Ensemble Transform Kalman Filter with a state-augmentation approach to estimate a small set of bias-correction factors. Two representative processes for the time evolution of the bias in the TIEGCM are tested: one in which the bias is constant and another in which the bias has an exponential growth and decay phase in response to strong geomagnetic forcing. We show that even simple approximations of the TIEGCM bias can reduce root-mean-square errors in 1-h forecasts of total electron content (a key ionospheric variable) by 20–45%, compared to no bias correction. These results suggest that our approach is computationally efficient and can be further refined to improve short-term predictions (∼1-h) of ionospheric dynamics during geomagnetic storms.

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