KEDS_Paper_Template
Knowledge Engineering and Data Science (KEDS) pISSN 2597-4602
Vol 3, No 1, July 2020, pp. 28–39 eISSN 2597-4637
https://doi.org/10.17977/um018v3i12020p28-39
©2020 Knowledge Engineering and Data Science | W : http://journal2.um.ac.id/index.php/keds | E : keds.journal@um.ac.id
This is an open access article under the CC BY-SA license (https://creativecommons.org/licenses/by-sa/4.0/)
Earthquake Magnitude and Grid-Based Location Prediction
using Backpropagation Neural Network
Bagus Priambodo
a, 1,
*, Wayan Firdaus Mahmudy
a, 2
, Muh Arif Rahman
a, 3
a
Faculty of Computer Science, Brawijaya University
Jl. Veteran no. 8, Malang 65145, Indonesia
1
baguspria@student.ub.ac.id *;
2
wayanfm@ub.ac.id;
3
m_arif@ub.ac.id
* corresponding author
I. Introduction
One of the inevitable disasters is a natural disaster. It may come without prior notice and has
been responsible for the massive scale of deaths [1]. Centre for Research on Epidemiology of
Disasters (CRED) reported an average of 77.144 deaths per year caused by natural disasters since
2000 to 2017 [2]. The natural disaster caused by seismic activities (earthquakes, tsunamis, and
volcanic activities) disrupted 3.4 million lives in 2018 [2]. Earthquakes have caused the most deaths
every year compared to other types of natural disasters, such as drought, flood, landslide, wildfire,
and many more—with a toll of 46.173 lives [2].
Even though it is inevitable, but it can be anticipated to minimize damage and casualties. Past
research has been conducted to predict the level of impact caused by earthquakes in real-time [2].
One of those past research is about an early earthquake warning system (EEWS), which will give an
alert when it detects an earthquake [3]. Numerous architectures and algorithms have been developed
in those studies. Various research is the utilization of neural network trained with backpropagation
algorithm and optimized using Levenberg (LOM) to predict hypocenter location, moment
magnitude, and the expansion of the earthquake [4], modification of LOM to minimize error on
EEWS [3], and utilization of neural tree to predict P and S waves [5].
Until now, to the knowledge of the author, the study of using a backpropagation (BP) algorithm
to predict earthquake magnitude and grid-based location in Indonesia has not been conducted yet.
This algorithm is chosen because it has been proven to perform well in broad types of problems,
such as regression, pattern recognition, and prediction [6][7][8][9]. In this paper, the study aims to
measure the performance of neural network trained using backpropagation algorithm in predicting
earthquakes magnitude and grid-based location based on earthquakes magnitude and location data in
Indonesia recorded from 2000 to 2019.
ARTICLE INFO A B S T R A C T
Article history:
Received 30 June 2020
Revised 2 July 2020
Accepted 15 July 2020
Published online 17 August 2020
Earthquakes, a type of inevitable natural disaster, is responsible for the highest
average death toll per year compared to other types of a natural disaster. Even
though it is inevitable, but it can be anticipated to minimize damage and casualties,
such as predicting the earthquake‘s magnitude using a neural network. In this study,
a backpropagation algorithm is used to train the multilayer neural network to weekly
predict the average magnitude of earthquakes in grid-based locations in Indonesia.
Based on the findings in this research, the neural network is able to predict the
magnitude of earthquakes in grid-based locations across Indonesia with a minimum
error rate of 0.094 in 34.475 seconds. This best result is achieved when the neural
network is trained for 210 epochs, with 16 neurons used in the input and output
layer, one hidden layer consisted of 5 neurons and a learning rate of 0.1. This result
showed backpropagation has pretty good generalization capability in order to map
the relations between variables when mathematical function is not explicitly
available.
This is an open access article under the CC BY-SA license
(https://creativecommons.org/licenses/by-sa/4.0/).
Keywords:
Neural network
Resilient backpropagation
Prediction
Magnitude
Earthquake
mailto:baguspria@student.ub.ac.id
mailto:wayanfm@ub.ac.id
mailto:m_arif@ub.ac.id
https://creativecommons.org/licenses/by-sa/4.0/
B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39 29
II. Methods
Numerous studies on the application of neural networks to predict incoming natural disasters
have been conducted before. One of them is to predict seismic activities (magnitude and other
seismic activities on a large scale—more than 5 Ritcher scale) [6]. The findings of that research
show acceptable performance (in terms of accuracy) of a 3-layer perceptron neural network model
trained using backpropagation, which achieved an accuracy of 80.55%. The other research done is
tsunami forecasting [10] using multi-layered perceptron neural networks and backpropagation
algorithm. The study shows that the backpropagation algorithm is a lot faster than many other
conventional models, and produced high accuracy in terms of predicting height and travel time of
tsunami based on earthquake location and size. Often neural networks are hybridized with other
techniques or algorithms. One example is the study done in 2006 [5], which used a neural tree to
pick up P and S waves faster, and more accurately: with precision score achieved is 0.96.
Numerous studies showed the performance of neural networks in predicting earthquakes. Neural
networks have been applied to an early earthquake warning system (EEWS), which is trained using
backpropagation with modified Levenberg-Marquardt algorithm to minimize the error rate in the
EEWS [3]. The error rate that was tried to be minimized was the error on seismic data amplitude
prediction based on the Chi-chi earthquake in Taiwan in 1999. Other studies showed increased
reliability dan responsiveness of EEWS when a neural network is applied [11]. A theoretical study
by [4] showed a prediction of hypocenter location, moment magnitude, and rupture size of an
earthquake is generated almost instantaneously as soon as the P wave is picked up.
A. Neural Network and Backpropagation
A neural network is a network that consists of connected neurons (to process inputs) whose job is
to produce an activation value [12]. This network mimics the structure and the ways of how the
human brain works, hoping it can be an artificial intelligence that is almost as intelligent as human—
by receiving, adapting, and transferring known and new knowledge and skills, as a lifelong learning
action [13]. A neural network consists of an input layer, zero or more hidden layer, and an output
layer. Information on neurons will be propagated through each layer, starting from the input layer,
all the way to the output layer [14][15]. The propagation is done by calculating the weighted sum on
each neuron, which then used as the input value for the activation function used. The result of the
activation function is then propagated to neurons in the next layer as inputs [11]. A common
example of activation function is sigmoid function (1) and hyperbolic tangent (tanh) function (2)
[16].
( )
(1)
( )
(2)
The backpropagation helped a neural network to learn the relationship between variables, without
explicitly defining the mathematical function that defines that relationship [17]. A neural network
trained using backpropagation is based on gradient descent [14]. In the backpropagation algorithm,
each neuron in the hidden and output layer will process its own inputs using the sum product of each
neuron‘s input value and weight of each neuron, respectively, which then processed through an
activation function (most popular used is sigmoid activation function) [17]. Then the error will be
calculated backward from the output layer to the input layer for the weight update process—this is
called: backpropagation. This weight update process usually used gradient descent to direct the
weight changes towards the minimum error [18]. During the weight update process, a parameter
called the learning rate (η), will determine the ―step width‖ of the updated value, which will finally
update the weight in order to upgrade the neural network‘s performance in terms of its
generalization capability. In the backpropagation process, the derivative of activation function and
error function is needed to calculate the weight change. In (3) and (4) each is the derivative of
sigmoid and tanh function, respectively.
( ) ( )( ( )) (3)
( ) ( ) (4)
The algorithm starts with the initialization phase. In this phase, all of the weights between
neurons will be randomly initialized (between 0 and 1). The learning rate will also be initialized,
30 B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39
which is usually set to be 0.1. After the initialization phase, the first data will be used as input to the
neural network, feedforward is then performed, which is calculating the weighted sum as input to
activation function on the next layer. This process is repeated until the output layer has produced the
predicted value. After the output layer has produced an output, the neural network will then begin its
backpropagation phase.
During the backpropagation phase, the partial derivative of the error function with respect to each
weight will be calculated, as the weights are directly contributed to the error. From the output layer
point of view, the weight from the hidden layer to the output layer contributed to input for activation
function in the output layer, resulting in a contribution to the error. From the hidden layer point of
view, the weight from the input layer to the hidden layer contributed to input for activation function
in the hidden layer, finally contributes to the error. Therefore, (5) will be used to calculate the
gradient/partial derivative of the error with respect to weights between the hidden and output layer,
and (6) will be used for the calculation of partial derivative with respect to weights between input
and hidden layer.
(5)
( )
(6)
( ) ∑
( ) ∑ ( )
In (5),
is the partial derivative of error concerning weights between the hidden and output
layer. It consists of
which is the partial derivative of input to the output layer (the weighted
sum value) for weight, and
is the partial derivative of the activation function in the output layer
to input on the output layer, while
is the partial derivative of error function to the output. When
each component is broken down, the final result of the formula is as in (5), the product of the output
of the hidden layer ( ), a derivative of activation function in the output layer (for example the
derivative of sigmoid; ( )) and derivative of the error function (10). Equation (6) is similar to
(5), but it showed the partial derivative for weights between input and hidden layer. When the
formula is broken down, the components are input value (input from training data; ), the partial
derivative of activation function in the hidden layer ( ( )), and the partial derivative of error
concerning output of the hidden layer will need further calculation steps. For partial derivative of the
error to the output of the hidden layer, it is the sum of all (written as ― ‖ which represents the
number of output neurons) partial derivative of the error with respect to particular output neurons
from neurons in the hidden layer. Thus, this component is actually the product of weights between
the hidden and output layer with
from (5). The partial derivative for biases is calculated likewise
(as in (5) and (6)), but in (5) and in (6) is changed into 1, because it is the derivative with
respect to bias.
{
The derivative of the error function used in this study (MAE) is shown in (7). This function can
only be differentiated only when the difference in predicted, and the target value is not 0. In the
context of (5), only one prediction and target value each used because in (5), it is only differentiated
with respect to one output neuron only. Later for programming purposes, to eliminate the possibility
of the unhandled case, the derivative will be 1 when the prediction is bigger or equal to the target
value.
After partial derivative for each weight (biases included), the weight will then be updated. The
weight update will follow the equation written in (8). The new weight for the next iteration ( (
B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39 31
)) will be the current weight ( ) reduced by the product of learning rate (η) and the partial
derivative of the error
( ). This feedforward, backpropagation, and weight update process will
be repeated until the maximum epoch has been reached, where one epoch is counted when all of the
training data has been fed to the neural network.
( ) ( )
(8)
B. Feature Extraction of Earthquakes
Earthquake is a geological phenomenon that happened because of the shifting of earth‘s plates,
caused by excessive pressure which the earth‘s crust cannot handle [19]. This excessive pressure
results in energy release in the form of waves that propagate through the earth‘s crust, causing
shockwave people can felt [20]. Those waves are picked up by seismographs. There are two types of
waves, P wave (the fastest wave), which will be received first by the seismograph, then followed by
a stronger wave, S wave, but slower than the P wave [21].
In seismograph, these two waves will produce data in 3 components: vertical, north-south, and
east-west motion. [22]. Based on data of P wave, S wave, epicenter, magnitude, and peak ground
acceleration (PGA), an early warning to civilians can be done, and emergency action can be
conducted earlier; this can be done with a tool: early earthquake warning system (EEWS).
In this study, the magnitude and location of earthquakes that happened in Indonesia, starting
from January 1 2000 until December 31 2019, will be used as an input feature. It was chosen based
on a previous study done by [23]. In that study, [23] used four features: earthquake number, location
(represented in numbers corresponding to grids), magnitude, and hypocenter depth. Date and time
are not used in that study because preliminary statistical analysis on the data used showed date and
time is not representative enough as a feature. In fact, it had too much unrelated/unneeded
information. Detailed location data—coordinates—is simplified into grids representing an area that
has been divided into 16 areas. Thus, the location feature consists of an integer number, ranging
from 0 to 15. Based on this, the current study also divides the location data used in this study into 16
grids, represented using an integer from 0 to 15, as seen in Figure 1. The area is divided into 16
rectangles with every rectangle has the almost equal area. There will be 2 rows and 8 columns used,
where starting from latitude of -10.909° to 5.907° will be divided into 2 sectors, and from longitude
of 95.206° to 140.976° will be divided into 8 sectors.
In another study, the magnitude of past earthquakes is used to predict earthquake magnitude for
the following day [6]. In that study, location is not used as a feature because it was assumed already
known beforehand. Therefore, magnitude and location (represented in grid numbers) of an
earthquake will be used as an input feature in this study.
Magnitude will be normalized using minmax normalization to match the characteristics of
sigmoid and tanh function, resulting in the more appropriate value. The result can then be
denormalized to get the actual predicted magnitude value by RB algorithm.
( )
( ) ( )
(9)
Fig. 1. Grid-numbering as location feature
32 B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39
In (9), the minmax normalization equation is shown. Variable x is the magnitude value that will
be normalized, min(x) is the minimum magnitude over all the data, and max(x) is the maximum
magnitude over all of the data. Based on (9), the denormalization equation can be inferred as in (10).
* ( ) ( )+ ( ) (10)
C. Evaluation Metrics
There are many methods that can be used to evaluate the performance of a neural network. In this
study, Mean Absolute Error (MAE) will be used as evaluation metrics. This metric calculates the
distance of predicted value and the target value. This metric is used based on a study done in [6] and
[23]. The equation for MAE can be seen in (11).
∑ | |
(11)
In (13), n represents the feature count,
represents target value of ith feature, and
represents
the predicted value of ith feature.
D. Proposed Method
The data is queried from United States Geological Survey (USGS) website and obtained in a
comma-separated value (CSV) file. The data collected is earthquake data that happened in
Indonesia, specifically inside these boundaries: latitude of -10.909° to 5.907°, and longitude of
95.206° to 140.976°. Earthquake events from January 1 2000 up to January 1 2019 will be used as
training data (36,453 records) and earthquake events from January 2 2019 up to December 31 2019
will be used as testing data (2,358 records). The data obtained (total of 38,811 records) is presented
as seismic data with 22 columns/attributes, and 4 of them will be taken as features in this study.
Those 4 attributes are detailed in Table 1.
After the data is obtained, there is two phases of preprocessing that will be done. First, ‗id‘
attribute will be added as feature to index each data. Then, the data on ‗time‘ column will be
changed to ‗date‘, because only the dates will be taken as the feature. The ‗latitude‘ and ‗longitude‘
feature will then be mapped into grid numbers (as explained before) and represented as ‗grid‘
feature. The last feature is taken as it is, which is the ‗mag‘ that represents the magnitude of the
event. A snippet of first 5 data after this first phase of preprocessing can be seen in Table 2.
After the preliminary phase of preprocessing has been done, the final phase will be done to create
a dataset that is ready to be used by the neural network. In this phase, each event will be grouped
into weekly-period data, and for each week, the average magnitude will be calculated for each grid.
This will be ‗avg_mag‘ feature. Then, this will be normalized using minmax normalization. The
final feature added will be the ‗target‘ feature, which is the ‗avg_mag‘ on the following week.
Table 1. Data attributes details
No Name Description Data Type Value
1 time Date and time in milliseconds &
UTC when the even occured.
Long integer [2000-01-02T12:46:58.770Z,
2019-12-31T09:50:41.876Z]
2 latitude Decimal degrees latitude. Decimal [-90,0, 90,0]
3 longitude Decimal degress longitude. Decimal [-180,0, 180,0]
4 mag Magnitude of the event. Decimal [-1,0, 10,0]
Table 2. Preliminary phase of preprocessing on the first 5 data
Id Date Grid Mag
1 1/2/2000 6 4.9
2 1/2/2000 6 4.4
3 1/3/2000 6 4.7
4 1/4/2000 14 4.4
5 1/4/2000 5 3.9
B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39 33
Snippet on first week data that has been preprocessed (earthquake events from January 2 2000 to
January 8 2000) can be seen in Table 3.
The neural network built will consist of 16 neurons in the input and output layer and one hidden
layer. The reason only one hidden layer is used is based on numerous study that has shown the good
result of approximation on any continuous function [17]. Each neuron in the input and output layer
represents the average magnitude in each grid. The input layer will receive data of the average
magnitude of earthquake events in a week, and the output layer will predict the average magnitude
for each grid in the following week. Each neuron in the hidden and output layer will have a bias with
uniform-value: 1.
III. Results and Discussions
In order to achieve the best result, there will be four components of the neural network
configuration that will be tested. The first one is to find the best maximum epochs allowed, then the
learning rate, the number of neurons needed in the hidden layer, and lastly, which activation
function will be used: sigmoid or tanh function.
In the maximum Epochs Testing testing, the maximum epochs allowed will be tested from 50,
then increased by 10 until no significant error rate change occurred. For current testing, the neural
network will use 5 neurons in the hidden layer, trained using the sigmoid function, a learning rate of
0.1, and all of the weights (except the biases) will be initialized with uniform value: 0.5. For each
number of maximum epochs tested, it will be tested ten times, and the average will be taken. The
average error rate and training duration will be measured to determine how many epochs that will
produce the best result. Figure 2 showed the lowest average error rate achieved is 0.093 when the
neural network is trained for 210 epochs, while the highest average error rate achieved is 0.094
when it is trained for 160 epochs. Figure 3 showed when a neural network is trained for 50 epochs, it
only took 12.93 seconds, but it took 134.49 seconds to complete training for 500 epochs.
Another parameter that may contribute to the performance of the neural network‘s generalization
capability is the learning rate. If it is too big, it may miss the optimal accuracy. If it is too small, the
learning process would be too slow. In the learning rate testing, the learning rate will be tested from
0.1, then increased by 0.1 until no significant error rate change occurred. For current testing, the
neural network will be trained using sigmoid function in 210 epochs (the best result achieved from
the last testing) using 5 neurons in the hidden layer, and all of the weights (except the biases) will be
initialized with uniform value: 0.5. For each learning rate tested, it will be tested ten times, and the
average will be taken. Figure 4 showed the lowest average error rate achieved is 0.093 when the
Table 3. Final phase of preprocessing on the first week data
Grid Avg_mag Target
1 0.699 0.000
2 0.000 0.000
3 0.000 0.000
4 0.000 0.000
5 0.582 0.000
6 0.627 0.594
7 0.658 0.603
8 0.000 0.637
9 0.000 0.000
10 0.767 0.616
11 0.676 0.621
12 0.651 0.000
13 0.000 0.000
14 0.589 0.548
15 0.562 0.644
16 0.616 0.000
34 B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39
neural network is trained using 0.1 as the learning rate, while the highest average accuracy achieved
is 0.103 when it is trained using 1.0 as the learning rate. The average training duration is not tested
in the current testing phase, as the learning rate does not affect directly to the training duration in
terms of computation time.
The next test measures the number of neurons needed in the hidden layer will be tested from 5
neurons, then increased by 2 until no significant error rate change occurred. For current testing, the
neural network will be trained using a sigmoid function in 210 epochs (the best result achieved from
the last testing) with a learning rate of 0.1, and all of the weights (except the biases) will be
initialized with uniform value: 0.5. For each number of neurons tested, it will be tested ten times,
and the average will be taken. The average error rate and duration will be measured to determine
how many neurons in the hidden layer that will produce the best result. Figure 5 showed the lowest
average error rate achieved is 0.093 when the neural network is trained using 5 neurons in the
hidden layer, while the highest average error rate achieved is 0.095 when it is trained using 23
neurons in the hidden layer. The average duration for training, as shown in Figure 6 is similar to the
result in the last testing: as the number of neurons increased, the training duration also increased.
The best training time achieved is 57.23 seconds when trained using 5 neurons only, and the worst
result is 75.06 when trained using 23 neurons.
Fig. 2.The average testing error rate for each maximum epochs value
Fig. 3. Average training duration for each maximum epochs value
B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39 35
Fig. 4. The average testing error rate for each learning rate value
Fig. 5. Average testing accuracy for each neuron count in the hidden layer
Fig. 6. Average training duration for each neuron count in the hidden layer
36 B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39
The Activation Function Testing used a sigmoid function or tanh function. For current testing,
the neural network will be trained in 210 epochs, the learning rate of 0.1, using 5 neurons in the
hidden layer (the best result achieved from the last testing), but all of the weights (except the biases)
will be initialized with a random value. For each number of maximum epochs tested, it will be tested
ten times, and the average will be taken. The sigmoid function produced an average error rate of
0.094, which is better than the tanh function, which produced an average error rate of 0.881. The
sigmoid function is also better than tanh function in terms of training duration, which only took
60.81 seconds compared to 60.91 seconds when trained using tanh function. Thus, the sigmoid
function will be used as the activation function.
Based on the testing results, when the neural network is trained with sigmoid as the activation
function in 210 epochs using 5 neurons in the hidden layer and 0.1 as learning rate, it achieves its
best performance with an error rate of 0.094 in 60.81 seconds. As shown in Figure 2, there is a
tendency in the beginning that as the number of maximum epochs increased, the average accuracy
also increased. This is also shown in Figure 7. Figure 7 shows the error rate of the neural network on
the training data for each epoch. It is shown that as it learns the data over and over at each epoch, it
became better in terms of error rate. This proves that the backpropagation algorithm is able to adapt
to the nature of data more accurately as it received more training [24]. This is where the weight
adaptation process in backpropagation takes part. Through the weight adaptation process, it finally
produces weights that are well-suited to the training data and reached better generalization capability
when tested against new data in the testing phase.
As the learning rate increases, it is shown that the performance of the neural network is getting
worse. This may happen because of the step width is too large that the local minimum may be
missed. It is also shown that as the number of neurons in the hidden layer increases, the performance
is not getting better. This may be the result of overfitting, a condition where the neural network
generalization capability became weak, and only achieve a good result when tested to the training
data only. Therefore, when the neural network is tested using the testing data, the error rate is higher.
The duration needed for the neural network to predict earthquake event magnitude (for events in
2019) is the total duration from training phase until testing phase; the prediction duration. The
average prediction duration is 0.005 seconds. Thus, overall duration needed is 60.81 seconds
(average training duration) plus 0.005 seconds (prediction duration): 60.815 seconds. This is the
total duration needed to predict 51 rows of data earthquake event magnitude based on training result
on 988 rows of data, with 16 features each.
As shown in Table 4, it is a snippet of prediction and target comparison for the first two weeks.
In prediction result, there are 12.99% (106 events) of the prediction that missed the target by 1 up to
5.2, and the others (about 87.01%—710 events) missed the target less than 1, with minimum error
recorded is 2.56 × 10
-4
.
Fig. 7. Error rate during training on each epoch
B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39 37
IV. Conclusion
There are two key findings in this study. First, to build input features based on magnitude and
location of earthquake event, detailed information of location (such as latitudes and longitudes)
needed to be mapped into grids, then the magnitude will be averaged weekly for each grid number.
The average magnitude weekly and based on each grid will be the input features. Secondly, the
lowest error rate achieved by backpropagation algorithm (when trained in 210 epochs using sigmoid
activation function, 5 neurons in the hidden layer and 0.1 as learning rate) to predict the magnitude
of earthquake event in the following week is 0.094, with 60.815 seconds needed for the neural
network to learn from 988 rows of data and predict 51 rows of data. Based on those key findings, it
is recommended to further studying the importance and impact of other input features, configuration
of the neural network, and hybridizing with other algorithms, which can further maximize the
performance in predicting earthquakes more accurately and in the small-time window to increase
preparedness.
Table 4. Comparison of prediction and target produced by resilient backpropagation algorithm
Date Grid Prediction Target
1/9/2019 1 4.524 4.767
1/9/2019 2 0.001 0.000
1/9/2019 3 0.001 0.000
1/9/2019 4 0.001 0.000
1/9/2019 5 4.398 4.200
1/9/2019 6 4.571 4.467
1/9/2019 7 0.004 4.400
1/9/2019 8 0.005 0.000
1/9/2019 9 0.002 0.000
1/9/2019 10 4.510 5.000
1/9/2019 11 4.285 4.300
1/9/2019 12 4.317 0.000
1/9/2019 13 4.482 4.540
1/9/2019 14 4.350 4.488
1/9/2019 15 4.308 4.467
1/9/2019 16 4.515 0.000
1/16/2019 1 4.519 4.300
1/16/2019 2 0.001 4.300
1/16/2019 3 0.001 0.000
1/16/2019 4 0.001 0.000
1/16/2019 5 4.386 4.433
1/16/2019 6 4.568 4.688
1/16/2019 7 0.003 0.000
1/16/2019 8 0.004 0.000
1/16/2019 9 0.001 0.000
1/16/2019 10 4.542 4.350
1/16/2019 11 4.302 4.300
1/16/2019 12 4.410 4.200
1/16/2019 13 4.499 4.975
1/16/2019 14 4.364 4.443
1/16/2019 15 4.316 4.450
1/16/2019 16 4.475 0.000
38 B. Priambodo et al. / Knowledge Engineering and Data Science 2020, 3 (1): 28–39
Declarations
Author contribution
All authors contributed equally as the main contributor of this paper. All authors read and approved the final paper.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit
sectors.
Conflict of interest
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
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I. Introduction
II. Methods
A. Neural Network and Backpropagation
B. Feature Extraction of Earthquakes
C. Evaluation Metrics
D. Proposed Method
III. Results and Discussions
IV. Conclusion
Declarations
Author contribution
Funding statement
Conflict of interest
Additional information
References
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