Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder
and Dense Network
Rayan Abri
1 a
, Harun Artuner
2 b
, Sara Abri
1 c
and Salih Cetin
1 d
Mavinci Informatics Inc., Ankara, Turkey
Department of Computer Engineering, Hacettepe University, Ankara, Turkey
Ionosphere, Total Electron Content, Deep Autoencoder, Deep Neural Networks, Linear Discriminant Analysis.
The ionosphere plays a critical role in the functioning of the atmosphere and the planet. Fluctuations and some
anomalies in the ionosphere occur as a result of solar flares caused by coronal mass ejections, seismic motions,
and geomagnetic activity. The Total electron content (TEC) of the ionosphere is the most important metric
for studying its morphology. The purpose of this article is to examine the relationships that exist between
earthquakes and TEC data. In order to accomplish this, we present a classification method for the ionosphere’s
TEC data that is based on earthquakes. Deep autoencoder techniques are used for the feature extraction from
TEC data. The features that were obtained were fed into dense neural networks, which are used to perform
classification. In order to assess the suggested classification model, the results of the classification model are
compared to the results of the LDA (Linear Discriminant Analysis) classifier model.The research results show
that the suggested model enhances the accuracy of differentiating earthquakes by around 0.94, making it a
useful tool for identifying ionospheric disturbances in terms of earthquakes.
As it slowly rises above the ground, it encounters an
atmospheric taxonomy in terms of height, and some
of the atmospheric layers have several key properties.
The ionosphere layer is one of the most significant
layers in the atmosphere, with numerous unique prop-
erties. These characteristics help to identify a wide
range of events, such as solar flares and earthquakes.
The ionosphere layer, which extends from 48 km to
960 km and is ionized into a plasma phase.
Total Electron Content is a primary quantifiable
statistic that identifies an ionosphere property (TEC).
TEC is a powerful tool for studying the ionosphere’s
morphology. TEC is described as the line integral of
electron density along a ray path or a metric of to-
tal electrons across a ray path in the literature. TEC
is measured in TECUs (TEC Units), with 1 TECU
equaling 1016 electron/m2 as described by (Arikan
et al., 2003)(Nayir et al., 2007). Measuring and moni-
toring TEC Values can describe the ionosphere layer’s
variations and turbulences effectively and efficiently.
According to (Nayir et al., 2007), the Global Po-
sitioning System (GPS) has provided a cost-effective
approach in estimating and analyzing TEC and mon-
itoring the ionospheric layer disruptions across a sig-
nificant fraction of the global continent during the last
several decades. The temporal and geographical vari-
ability of the ionosphere layer is closely related to
the earth’s daily (every day) and yearly rotation, as
well as the pattern of magnetic field lines of the ge-
omagnetic dipole. Even when there are no geomag-
netic events, the earth’s magnetic field is not silent, as
(Rishbeth and Garriott, 1969) describes. Fluctuations
in geomagnetic and solar activity, as well as seismic-
ity, affect the ionosphere’s quiet circumstances. As
a result, these repercussions may produce changes in
parameters such as earthquakes.
In summation, this paper offers a model based on
deep learning approaches for interpreting the link be-
tween earthquakes and ionosphere perturbations, with
two sub-tasks of feature extraction and classification.
The model’s initial phase focuses on using Deep Au-
toencoders and unsupervised learning approaches to
extract features from TEC data. The suggested clas-
sification using deep dense neural networks based on
the supervised learning approach is the second step.
Abri, R., Artuner, H., Abri, S. and Cetin, S.
Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network.
DOI: 10.5220/0011332900003269
In Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022), pages 158-167
ISBN: 978-989-758-583-8; ISSN: 2184-285X
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
The following is the rest of the story. The related
works on Total Electron Content variation produced
by magnetic and earthquake disruptions in the iono-
sphere layer are discussed in Section 2. The iono-
spheric dataset and prepossessing stages for the pro-
posed models are presented in section 3. In Section
4, we show our classification model for ionospheric
data. Sections 5 and 6, respectively, cover the eval-
uation technique and outcomes. The final comments
and future studies are found in section 7.
The relevant studies for observing ionospheric pertur-
bations are presented in this section. Geomagnetic
events such as storms and earthquakes may cause se-
vere disruptions in the electron density distribution.
The seismo-ionospheric anomalies as stated by (Pu-
linets et al., 2018; Tao et al., 2017) have been inves-
tigated using satellite-based observations from GPS
The ionospheric disruption and storms may cause
severe turbulence in the ionosphere’s TEC values was
explored by (Biqiang et al., 2007; Trigunait et al.,
2004). Furthermore, earthquakes and seismic activ-
ity, according to (Pulinets et al., 2003), might produce
changes in electromagnetic signals and the chemical
properties of the atmosphere in the troposphere, and
Many studies, such as those cited in (Liu et al.,
2004; Le et al., 2011; Liu et al., 2006; Liu et al.,
2010), concentrate on statistical evaluations of the
empirical association between ionospheric based ab-
normalities and earthquakes. For example, in (Liu
et al., 2004), the authors looked at the TEC associ-
ated with 20 strong (Magnitude 6.0) earthquakes
in Taiwan over the course of four years (1999–2002).
During the other quiet days, they discover irregulari-
ties that show the TEC value is decreasing five days
before the earthquakes. Furthermore, (Le et al., 2011)
shows the relationship among TEC-based ionospheric
abnormalities and severe earthquakes (Magnitude
6.0) throughout a nine-year period (2002–2010).
They also verified that earthquakes with a strength of
7.0 and a depth of 20 km cause a high percentage
of anomalies in the ionosphere.
(Ulukavak and Yalcinkaya, 2017; Pundhir et al.,
2017; Oikonomou et al., 2016) have recently ob-
served aberrant ionospheric layer changes based on
TEC values measured days and hours before severe or
large earthquakes. The data for the TEC was collected
from a GPS station in the earthquake zone. There
are some questions concerning how to create such
anomalies near the epicenter of powerful earthquakes.
According to (Tariq et al., 2019), and (Shah and Jin,
2015), there are direct links between ionosphere va-
riety and earthquakes. TEC values acquired from
the GPS receiver varied and rose before Magnitude
6.0 earthquakes occurred throughout the period
1998–2014. According to the authors of (Le et al.,
2011), ionospheric TEC abnormalities were used to
categorize severe and strong earthquakes based on
their magnitudes.
We plan to extract significant aspects of earth-
quakes using TEC values in the ionosphere layer,
and then identify earthquake days in the target sta-
tion zone, based on previous research in this area.
Our main emphasis is on extracting characteristics
from earthquakes and classifying them using TEC
data from the ionospheric. We are not focusing on
forecasting earthquakes in previous days at this time,
and our study is focused on TEC changes during mod-
erate and severe earthquakes.
The dataset relating to earthquakes and GPS station
TEC values was introduced in this part. The proce-
dures for preparing the dataset are also described.
3.1 Dataset
TEC values derived from GPS stations, as described
in Section 2, are a valuable approach to assessing the
ionospheric reaction to earthquakes and solar storms.
The ionospheric variability is investigated using TEC
data collected from GPS stations (Dual-Frequency
GPS receiver). TEC data was acquired from two
GPS sites for this study. This information was gath-
ered from the IONOLAB group (Hacettepe Univer-
sity of IONOLAB is an organization of electrical
engineers to investigate hurdles of the ionosphere.)
. The first station is placed at coordinates (Lat :
20.15,Lon : 70.13) Figure 1 shows the city of
Iquique in Chile. The second station is located at co-
ordinates (Lat : 20.85, Lon : 117.1) Figure 1 depicts
Karratha, a town in Western Australia’s Pilbara area.
The earthquake data is gathered by the (United States
Geological Survey of Earthquakes)
This research examines ionospheric fluctuation
across moderate and severe earthquake occurrences
of varied magnitudes from 2012 to 2019. The data
Available at
Available at
Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network
Figure 1: The Iquique (Chile) and station and the Karratha
(Western Australia) station.
Figure 2: The Iquique and Karratha stations located in the
same latitude from the two hemispheres of the east and west
of the earth.
for each station was gathered between 2012 and 2019.
The data was divided day by day with 2880 TEC sam-
ples in a day. The stations are positioned in the same
latitude from the east and west hemispheres of the
earth, as shown in Figure 2. The Chile area is known
to powerful and severe earthquakes, however the Kar-
ratha region is rather earthquake-free.
3.2 Data Preparation
The reliability of the input dataset is crucial in deep
learning ideas because there is a direct link between
the reliability of the input data and the effectiveness of
the trained model. Data preprocessing includes data
tuning procedures that transform raw input data into
a usable format. Ionospheric TEC data is often inad-
equate, inconsistent, and prone to many inaccuracies.
In this study data preprocessing is divided into three
stages: cleaning, transformation, and reduction.
The cleaning stage aims to delete missing values
and perform regressions using previous and later ob-
servations samples to smooth noisy values since raw
TEC data on certain days has missing and noisy val-
ues. Furthermore, the gathered data is transformed
into suitable mining formats. TEC data are scaled to
a specific defined range to accomplish the normaliza-
tion (0,1.0). A day’s value of TEC data contains of
2800 samples. Learning enormous volumes of input
data with various characteristics in data mining makes
analysis problematic or impossible, and the training
process might take a long period of time. Data Re-
duction is a collection of approaches for reducing the
number of input samples in a dataset without harm-
ing the integrity of the original data. A single day’s
data is reduced from 2800 to 95 by sampling TEC
values every fifteen minutes. The ionospheric and
EQs(earthquakes) datasets are detailedly described in
Table 1. All of the earthquakes were recorded at sta-
tions within a 250-kilometer radius.
The gathered data is shown in the table as well as
information such as the ratio of the train and test sets.
Table 1: Detailed information of the dataset.
Dataset Characteristics Value
Day count in uncleaned Dataset 2922
Day count in cleaned Dataset 2571
Train-Test Ratio 80%-20%
Total EQs4.5 141
Total EQs5.0 91
Day count in Trainset 2057
Day count in Testset 514
EQs 4.5 in Trainset 113
EQs 5.0 in Trainset 73
EQs 4.5 in Testset 28
EQs 5.0 in Testset 18
Solar flares and other cosmological event compo-
nents have been shown to influence the ionosphere in
early research. Xray fluxes are increased during solar
flares, and this has been recognized as the source of
increased ionization in the ionosphere. Although, this
research focuses on ionospheric changes and TEC
fluctuations during various earthquakes, it is neces-
sary to diminish the impact of solar flares and other
cosmological events in order to identify earthquakes
and geomagnetic activity more accurately.
As previously stated, the stations are placed at
the same latitude as the earth’s east and west hemi-
spheres. Because solar flares and other comparable
cosmic occurrences influence both the east and west
hemispheres of the planet, the similarity between two
places may be estimated. Solar flares and other such
cosmic occurrences are shown by anomalies on the
same day in both the stations. It is possible that the
related stations contain the same irregularities as a re-
sult of solar flares and other cosmic phenomena. To
reduce the impact of these abnormalities, it is neces-
sary to compute the similarity between the stations.
The similarity among coincident days at each re-
gion in the dataset is calculated for this objective. Be-
cause of the structure of the dataset, the similarity be-
tween coinciding days at each station is estimated us-
ing cosine similarity. Cosine similarity is a measure
that compares two non-zero vectors and is defined by
the cosine of their angles. Equation 1 is used to com-
pute the cosine similarity.
cos(x,y) =
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
Where x and y are the TEC data vectors for each
day in the stations.
The framework of the suggested model is given in this
section. A broad overview of the suggested model is
shown in Figure reffig:model. Deep neural networks
are utilized because of the vast quantity of TEC data
associated with each station. Because Deep Learn-
ing’s core benefit is the study and learning of huge
volumes of supervised and unsupervised data, it’s par-
ticularly useful for Big Data Analytics, where data
is mostly unlabeled. Unsupervised learning methods
such as Deep Autoencoders and deep dense networks
are employed in the proposed model.
Figure 3: Overview of the proposed model.
4.1 Feature Extraction
The main feature extraction techniques presented by
(Khalid et al., 2014) are based on projection and data
mapping from a complicated input space with many
dimensions to a new output space with fewer dimen-
sions while minimizing data loss. (Dem
sara et al.,
2013; Sharma and Paliwal, 2015) discuss two promi-
nent projection methods: Principal Component Anal-
ysis (PCA) and Linear Discriminant Analysis (LDA).
The PCA technique projects the input data into its pri-
mary directions by maximizing variance. This tech-
nique is classified as an unsupervised technique. The
LDA, on the other hand, optimizes discriminating
data from input classes in order to produce a linear
output space. The LDA technique is characterized
as a supervised learning technique. The fundamental
difficulty with the techniques outlined is linear pro-
jection. (Lopez-Paz et al., 2014) propose non-linear
kernel functions as a solution to this problem.
Due to the large dimensionality of ionospheric
TEC data, lowering dimensionality to create a com-
pressed feature set is considered an important step in
the feature extraction process. Traditional machine
learning algorithms should theoretically be capable
of operating on any number of characteristics. These
models with large-dimensional datasets are exposed
to issues like as over-fitting of the training set, exces-
sive computational cost, and the dimensionality curse.
Autoencoders use neural networks to reduce the input
dimensions, with the goal of minimizing reconstruc-
tion loss. As a result, adding hidden layers to the au-
toencoders causes the dimension reduction process to
work properly. Deep Autoencoders have been shown
to be successful in detecting non-linear features in a
variety of scenarios.
Deep Autoencoders are multi-layer neural net-
works that produce the desired output from the input.
Within a pair of encoding and decoding steps, an au-
toencoder learns a map from the input to itself. For
feature learning, autoencoders have recently directed
to unsupervised approaches. It attempts to learn a
condensed description of the input while maintaining
the most critical data.
X = Dcr(Enc(X)) (2)
Where X represents the input TEC data, Enc rep-
resents an encoding map from the TEC data to the
hidden layer, Dcr represents a decoding map func-
tion from the code layer to the output layer, and barX
represents the recovered a similar version of the TEC
data in Equation 2. The goal is to teach Enc and
Dcr to reduce the difference between X and barX as
much as possible. To reduce the error of reconstruc-
tion of input from hidden code nodes, the encoder and
decoder functions (Enc and Dcr) are trained concur-
rently. An autoencoder might be seen as a possible
reason for the optimization issues.
k X Dcr(Enc(X )) k (3)
In Equation 3, k . k is commonly considered to be the
The input, encoder, decoder, and output layers are
shown in Figure 4. The autoencoder may output a
more compact vector as input vector if the reconstruc-
tion error is reduced. Sigmoid and ReLU, as defined
in Equations 4 and 5, are employed in this study.
Figure 4: A Deep Autoencoder with five layers.
Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network
ϕ(z) =
1 + e
R(z) = max(0,z) (5)
The output of the x
node in the i
layer is obtained
sequentially from the output of the prior nodes in the
previous layer as Equation 6 where bias
is bias scalar
and N
represents the number of nodes in the i
is the weights which connect the x
node in the
layer to the n
node in previous layer.
= ϕ(net
) = ϕ(
+ bias
) (6)
Increasing the number of layers improves the ca-
pacity to learn more complex patterns. The matrix of
all weights W is changed to minimize the mean square
error over the training set, as shown in Equation.
ε =
k X Dcr(Enc(X )) k
4.2 Classification Method
After the feature extraction, these features must be
classified. Several different classification methods
have been used in the literature. Deep neural networks
are often used in combination with softmax regres-
sion. It is possible to combine the classifier and en-
coder functions for this purpose. Logistic regression
is induced by softmax (multinomial logistic) regres-
sion. It uses Equation 8 to calculate the probability of
the i
= Prob(i|I) =
i = 1,...,N (8)
Where w
are the training weights for the i
class and
I is the classifier’s input. The classification is carried
out by comparing Prob
s. The softmax may simply
be used with the encoder function to form the net-
work’s deep structure.
Figure 5 shows a block diagram of the suggested
autoencoders. It’s a combined model that includes
both unsupervised and supervised learning. The en-
coder that was learned in the feature extraction step is
used for unsupervised learning, while the supervised
model is a dense softmax classifier.
By decreasing the error rate and employing Equa-
tion 7, features at hidden layer two are reduced and
compressed. These characteristics are then supplied
to the next autoencoder layer, and the features from
layer three (code layer) are fed to the softmax classi-
fier, which uses labeled data to do classification. Dur-
ing the feature extraction step, the layers of the Deep
Autoencoder are trained individually. As a result, the
features are learnt unsupervised, whereas classifica-
tion is done supervised.
Figure 5: The structure of the proposed model.
By using learning parameters and evaluation metrics
discussed in this section, you will be able to better
grasp the metrics necessary to conduct a quality as-
As previously noted in Section 4; the TEC val-
ues are split by days and classified using the sug-
gested model in the technique section, as previously
described. The suggested model is broken into two
sub-models, which are described below. The Deep
Autoencoder is used to extract the features from TEC
values in terms of days in the first sub-model. When
the feature extraction phase is trained, it provides
a low-dimensional form that encodes a meaningful
topological structure of TEC characteristics in order
to reconstruct the high-dimensional input. The sec-
ond kind of dense network is a softmax dense net-
work, which is pinned to the encoder and is used to
do classification jobs. The hidden layers of the clas-
sifier are bound by the earthquake labels in the data.
By combining the autoencoder with the classifier, the
networks are trained.
When evaluating the performance of a model,
it is necessary to use model assessment measures.
The model determines which evaluation measures are
used and which ones are not. As a result of the nature
of the data and the suggested model, we employ the
accuracy, precision, and recall of the model as perfor-
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
mance indicators. The accuracy of classification mod-
els is a frequent assessment parameter in this context.
It is defined as the ratio of the number of correctly
identified earthquakes to the total number of correctly
classified days. Precision defined as the ratio of accu-
rately identified earthquake days to the total number
of days labeled as earthquake days in a year. The re-
call is a measure of how well our model performs in
properly detecting earthquake days when compared to
the total number of earthquake days. Depending on
the data and requirements of the classification model,
it may be necessary to prioritize precision and recall
over the other. The nature of this study is the cate-
gorization of ionosphere disturbances based on earth-
quakes. In addition to earthquakes, ionospheric dis-
turbances may influence other types of phenomena.
Therefore, the number of false positives may be ac-
ceptable in the classification model.
A relationship or trade-off exists between preci-
sion and recall values in conventional classification
models. The term ”F1-score” refers to a measure of
test set efficiency that takes into account both preci-
sion and recall while calculating the score. The F1-
score is the harmonic average of precision and recall
in Equation 9.
score = 2
precision recall
precision + recall
It is possible to forecast the performance of ma-
chine learning algorithms by dividing a dataset into
two parts: the train set and the test set. As shown in
Table 1, the dataset has been divided into two groups:
train sets and test sets, with an 80-20 split ratio be-
tween the two groups. It seems that there is no appro-
priate ratio between the number of quiet days and the
number of earthquake days in the dataset.
As a consequence, there are only 141 earthquakes
with magnitudes greater than or equal to 4.5 between
2012 and 2019. A limited number of earthquake
classes are provided in the classification model. With
just a limited number of earthquake classes included
in the classification model, this results in an issue
known as imbalanced classification, which is a dif-
ficulty in the model. When a class is relatively rare in
comparison to other classes, the class imbalance issue
emerges. There have been several approaches devel-
oped for unbalanced classification by (Makki et al.,
2019; Lin et al., 2020), and some beneficial findings
have been published in the literature.
In order to deal with the issue of unbalanced
classes, K-fold cross-validation has been used. In the
k-fold cross-validation approach, model performance
is evaluated by splitting the data into n equal folds and
then measuring the performance of the model on each
fold. This technique is similar to that of the repeated
random sampling technique. In an unbalanced class
distribution situation, the proper use of k-fold cross-
validation needs the usage of stratified k-fold cross-
validation, which is described below. In particular, it
has the ability to divide arbitrarily in such a manner
that the same class distribution is maintained in each
subset. Testing is carried out using a ten-fold cross-
validation strategy for the purpose of analyzing the
days. In order to make each fold a suitable sample of
the original dataset, 10 equal folds are randomly split
into the original dataset.
To determine the significance of the suggested
combination model, we employed an LDA (Linear
Discriminant Analysis) classifier based on the com-
pression of the features, similar to the work done by
the authors (Kim et al., 2011; Tharwat et al., 2017).
Linear discriminant analysis is implemented with the
aid of a max function M, which serves as a classifica-
tion rule.
(X) =
(X µ
(X µ
M(X) = X
+ log(p
) (11)
(X) represents the conditional density of X in
class i, where p
is the probability of class i. If the
vector of features X is variable and distributed with
a mean vector µ
and a shared covariance matrix
then the solution is as follows: Once f
(X) has been
determined, as indicated in Equation 10, f
(X) may be
calculated. Calculation of the discriminant function
M(X) is done using the Bayes rule, which results in
Equation 11.
The performance of our suggested model, DAEclass
(Deep Autoencoder Classifier), as well as the LDA
classifier model, is assessed using the evaluation ap-
proach stated in Section 5. Specifically, the purpose
of this section is to evaluate and analyze the perfor-
mance metrics of both the suggested classification
model and the LDA classifier.
Table 2 gives the percentage of performance mea-
sures such as accuracy, recall, precision, and F1-score
for the proposed DAEclass model and LDA classi-
fier based on two datasets. On the first and second
Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network
Table 2: Comparison of DAEclass and LDA classification models using performance metrics.
Model Accuracy Precision Recall F1-Score
DAEclass-EQs4.5 0.93 0.56 0.75 0.64
LDA-EQs4.5 0.89 0.30 0.63 0.40
DAEclass-EQs5.0 0.96 0.61 0.83 0.70
LDA-EQs5.0 0.94 0.35 0.71 0.47
rows, you can see a comparison between the pro-
posed model and the LDA classifier model, which
is based on all earthquakes with a magnetite 4.5
value in their dataset. It is undeniable that the sug-
gested model outperforms the LDA classifier across
the board in all criteria. The suggested model im-
proved its precision and recall over the LDA classi-
fier. The fluctuations in the ionosphere layer are a re-
flection of all cosmic and seismic activity. Precision
is lower than recall and accuracy because the false
positive count is high high owing to the unbalanced
classes in the dataset. The final two rows compare the
proposed model to the LDA model, which is based
on more powerful earthquakes (magnetite-EQs 5.0)
than those in the first two rows. Because severe earth-
quakes have a greater impact on the ionosphere than
mild earthquakes, they are more clearly characterized.
The performance difference between the two models
is lower than the performance gap between the two
models on the EQs 4.5 dataset.
EQs4.5 and EQs5.0 datasets are represented
by the bar chart in Figure 6, which shows the com-
parison of performance measures between two mod-
els. It can be observed in Figure 6 (A) that the accu-
racy of the DAEclass increases marginally in the two
datasets that have been described. Precision and recall
measures are helpful indicators of classification per-
formance when the classes are unequally distributed
over the dataset. As a consequence of earthquakes
and false alarms, precision is a measure of classifi-
cation result relevance, while recall is the classifier’s
capacity to locate all of the earthquake days. The
performance of the DAEclass classifier is more sta-
ble when compared to the LDA classifier, as shown
by the comparison of the two databases. Overall, the
DAEclass technique significantly improves precision,
recall, and F1-score, as shown in Figure 6 (B)(C)(D).
This results in a greater extent of better performance
in the dataset EQs4.5 than the dataset EQs5.0, and
the DAEclass is more trustworthy in all earthquakes
as a result of this.
For the purpose of comparing multiple classifiers,
it might be advantageous to summarize the perfor-
mance of each classifier into a single metric. The
Receiver Operating Characteristic (ROC) curve is a
commonly used and standard metric for comparing
various classification models. It is calculated by com-
puting the area under the curve. The relationship be-
tween the true-positive rate and the false-positive rate
was shown by the receiver operating characteristic
curve (ROC curve).
Classifiers that produce curves that are closer to
the top-left corner of the screen demonstrate more de-
pendable performance. The ROC curves for the DAE-
class and LDA classifier models, which were con-
structed using two datasets, are shown in Figure 7.
Because the ROC curves associated with the DAE-
class are positioned increasingly closer to the upper
left angle in ROC space, the DAEclass has a grad-
ually more significant discriminant ability for earth-
quake classification as time goes on. As shown in the
image, the DAEclass and the LDA classifier may be
visually compared at the same time, with the results
indicating that the DAEclass is more efficient than the
LDA classifier in this case.
In order to evaluate the intrinsic performance of
classification models, the AUC (area under the curve)
is an effective and integrated measure of the true-
positive and false-positive rates. In other words, the
bigger AUC indicates that the diagnostic test is ideal
for distinguishing between earthquake and quiet days.
The outputs are shown in Table 3, which shows the
AUC values of the model for each of the datasets.
The AUC in the DAEclass-EQs4.5 classifier is 0.89,
while the AUC in the LDA-EQs4.5 classifier is 0.79,
indicating a significant increase in the ROC metric
in both classifiers. On the dataset EQs5.0, the
AUC values for the DAEclass technique are some-
what higher than those for the LDA classifier, ranging
from 0.85 to 0.91.
Table 3: AUC values for the DAEclass and the LDA classi-
DAEclass-EQs4.5 0.892
LDA-EQs4.5 0.794
DAEclass-EQs5.0 0.914
LDA-EQs5.0 0.856
A statistical test is used to examine the suggested
model in further detail, in order to determine its de-
gree of significance and potential for improvement. In
the test, the null hypothesis assumes that there is no
difference between the categorized earthquake days
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
Figure 6: Performance metrics of the DAEclass and the LDA models based on two datasets (EQs 4.5 and EQs 5.0).
Figure 7: ROC curve of the DAEclass and the LDA classifier models based on two datasets (EQs4.5 and EQs5.0).
and the quiet days. In order to determine whether or
not the null hypothesis under consideration can be re-
jected, the p-value test is done. The probability of de-
tecting the effect(E) when the null hypothesis is true is
represented by the P-Value. As shown by the test re-
sults of the DAEclass model, the difference in perfor-
mance across all measures is statistically significant
(p value 0.01).
This study presents a method for interpreting earth-
quakes based on TEC values in the ionospheric layer.
Data on ionospheric TEC was gathered from two
GPS sites. The Chilean area is prone to powerful
and severe earthquakes, however the Karratha region
is rather earthquake-free. The primary goal of this
study is to establish a link between earthquakes and
ionosphere disturbances using deep learning methods
to accomplish two sub-tasks: feature extraction and
classification. Deep learning techniques are used to
accomplish this goal. In the first stage, we concen-
trate on feature extraction from the TEC data, which
is accomplished via the use of an unsupervised learn-
ing algorithm. To extract relevant information about
the earthquake and quiet days, we create a Deep Au-
toencoder. Then, in the following stage, we use a su-
pervised learning approach to do classification using
a dense neural network, which is followed by a fi-
nal step. The LDA classifier was used to examine
the contribution of the proposed combination model
to the overall contribution. Our findings show that
the two test sets of earthquakes, which included mild
and severe earthquakes, performed roughly 90-94 per-
cent accurately based on the correctness of the data.
In terms of accuracy, recall, precision, F1-score, and
ROC curve metrics, the proposed DAEclass outper-
forms the LDA in a more trustworthy and dependable
Because the primary purpose of this study is to ex-
tract important aspects of earthquakes from the iono-
sphere layer using TEC values and to identify earth-
quake days in the target station zone, earthquake pre-
diction is not within the scope and priority of this re-
search. In our other work (Abri and Artuner, 2022),
we planed to work on a predictive system to predict
earthquake using TEC data related to the earlier days
of earthquakes by LSTM based deep learning models.
Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network
This research is supported by Mavinci Informatics
Inc. in Turkey, an R&D company in information and
communication technologies, security and defense ar-
eas with the capability of software development, arti-
ficial intelligence, and machine learning. This article
is a thesis article and produced from the Ph.D. thesis
(Abri, 2021).
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Big Data Analysis of Ionosphere Disturbances using Deep Autoencoder and Dense Network