See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/341635348
LSTM-based Models for Earthquake Prediction
Conference Paper · March 2020
DOI: 10.1145/3386723.3387865
CITATIONS
READS
41
1,675
3 authors, including:
Asmae Berhich
Mohammed Issam Kabbaj
Ecole Mohammadia d'Ingénieurs
Ecole Mohammadia d'Ingénieurs
7 PUBLICATIONS 127 CITATIONS
33 PUBLICATIONS 670 CITATIONS
SEE PROFILE
All content following this page was uploaded by Asmae Berhich on 21 August 2022.
The user has requested enhancement of the downloaded file.
SEE PROFILE
LSTM-based Models for Earthquake Prediction
Asmae BERHICH, Fatima-Zahra BELOUADHA, Mohammed Issam KABBAJ
AMIPS research team, E3S research center
Mohammadia school of engineers, Mohammed V University in Rabat, Morocco
berhich.asmae@gmail.com, belouadha@emi.ac.ma, kabbaj@emi.ac.ma
ABSTRACT
Over the last few years, many works have been done in earthquake
prediction using different techniques and precursors in order to
warn of earthquake damages and save human lives. Plenty of works
have failed to sufficiently predict earthquakes, because of the
complexity and the unpredictable nature of this task. Therefore, in
this work we use the powerful deep learning technique. A useful
algorithm that captures complex relationships in time series data.
The technique is called long short-term memory (LSTM). The work
employs this method in two cases of study; the first learns all the
datasets in one model, the second case learns the correlations on
two divided groups considering their range of magnitude. The
results show that learning decomposed datasets gives more wellfunctioning predictions since it exploits the nature of each type of
seismic events.
CCS Concepts
• Computing methodologies → Machine learning → Machine
learning approaches → Neural networks.
Keywords
Prediction; earthquakes; LSTM; time series data; deep learning.
1. INTRODUCTION
The application of ANN, RNN, and DNN models is present in
earthquake prediction where many works have emerged using
different techniques, models and data sets of different areas.
However, many of them were not capable to make a reliable
prediction especially in the case of large earthquakes. They
couldn’t capture the correlations in the datasets because of their
small number in the studied areas.
In this paper, we present our work of earthquake prediction using
the data sets of Morocco since it is not immunized from the risks of
earthquakes and their disastrous consequences. Earthquakes
generate significant human and material damage and their costs are
in billions of Dirhams. The case of Al Hoceima, which experienced
a tragedy in 2004 is an example. The data set of Moroccan
seismicity from 1900 to 2019 is given by the National Geophysical
Institute of the National Centre for Scientific and Technical
Research CNRST.
This work is based on the application of the deep learning technique
Long short-term memory (LSTM), which is widely used to classify,
process and predict time series data problems. LSTM is a variant of
recurrent neural networks known by their ability to model sequence
data and to remember past data in memory.
Earthquakes can suddenly strike any region in the world; they lead
to great damages depending on their magnitudes. Earthquakes with
large magnitudes could be potentially fatal and cause serious
economic and material losses. The medium earthquakes are also
dangerous especially for countries that are not taking the necessary
precautions. Warning from earthquakes was and is still a
challenging problem that needs more in-depth researches and
suitable solutions. Many works on earthquake predictions have
been done for many years. Even that it was considered an
impossible task, machine learning and deep learning challenges
have allowed realizing this task and making it possible. From 1994
till now seismologists and scientists are trying to solve the
earthquake prediction using neural networks and deep learning
[19]. However, a lot of works do not fill the full meaning of
earthquake prediction. Seismologists had defined earthquake
prediction by providing [1]:
By this powerful algorithm, we model our seismic datasets in two
cases of study. The first one gives the prediction of the magnitude,
location and year of the incoming earthquakes using the whole
dataset, the second case focuses on datasets decomposition based
on their magnitude range. The decomposition makes two groups of
the dataset for prediction. The first group presents large
earthquakes, and the second presents medium and small
earthquakes. The models of both cases are evaluated and compared
in the following sections. Section 2 presents the classification of
previous works that have been applied in earthquake prediction.
The LSTM model architecture is explained in section 3. Section 4
describes our datasets and introduces our proposed methodology
and model. The performance of our models is evaluated and
discussed in section 5. Finally, the last section concludes in brief
the aim of our paper.
a specific time range, a specific location, a specific magnitude
range and the probability that performs the prediction.
In this section, we present the previous works classified into four
categories:
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies
bear this notice and the full citation on the first page. Copyrights for
components of this work owned by others than ACM must be honored.
Abstracting with credit is permitted. To copy otherwise, or republish, to
post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from Permissions@acm.org.
NISS2020, March 31-April 2, 2020, Marrakech, Morocco
© 2020 Association for Computing Machinery.
ACM ISBN 978-1-4503-7634-1/20/03...$15.00
https://doi.org/10.1145/3386723.3387865
Works based on precursor signals, this technique uses the natural
effects and their abnormal behaviors, for example, Fan et al [6]
present an earthquake prediction approach based on extracting the
texture and emergence frequency of clouds and estimate the
possible location of earthquakes. Florido et al [7] analyze large
seismic events of Chilean zones to detect precursory patterns for
large earthquakes using clustering algorithms. Hayakawa [9]
Propose to utilize the electromagnetic phenomena as a short-term
precursor and presented the history and the reason for using these
precursors. Hayakawa and Yamauchi et al [10] monitor the milk
2. RELATED WORKS
yield of cows at Kagawa, and they find an abnormal depletion about
10 days before an earthquake, they discuss and compare this
behavior with electromagnetic precursors.
Works based on statistical and mathematical approaches like in
[14] Kannan identify patterns within the random occurrences of
multiple seismic zones like California, central USA, Northeast
USA, Hawaii, Turkey, and Japan using Poisson distribution and
spatial connection model. Pasari [25] employs probability
distributions such as Weibull and gamma models to observe the
cumulative probability of magnitude 6.0 or higher in the northern
Himalaya. Sitharam et al [26] use three models lognormal, Weibull
and gamma to estimate the probability of occurrences in six
different seismic regions of different tectonic features. They find
that a higher value of ln L guarantees a performant model using the
logarithmic probability of the likelihood function. Boucouvalas et
al [5] produce a modified version of the mathematical technique
FDL based on Fibonacci, Dual and Lucas numbers, in order to
predict the location of the epicenter of an earthquake.
works based on machine learning and artificial neural networks
(ANN), such as Su and Zhu [29] who build an ANN model to study
the correlations between the maximum of earthquakes affecting
coefficient and influencing factors like basemen rock and site
condition. Ni and Zhou [22] apply A model for damage detection,
where they applied a principal component analysis (PCA) based
data reduction technique to the measured frequency response
functions FRFs as the input variables of ANN instead of the raw
FRF data. Asim et al [2] elaborate a machine learning classifiersbased model, it utilizes the eight calculated features from the
geophysical parameters of Gutenberg-Richter’s inverse law. Four
classifiers are applied and compared neural networks, recurrent
neural networks, random forest, and linear programming boost
ensemble classifiers in order to predict earthquakes magnitude for
the Hindukush. Moreover, Asim et al [3] Compute seismic
indicators to consider the maximum of information of seismic
activity in different regions, then they apply the genetic
programming and Adaboost (GP-Adaboost) as an ensemble
method to predict earthquakes of magnitude 5.0 and above. And in
[20] the same authors calculate sixty seismic features using
geophysical and seismological concepts, then a support vector
machine (SVM) regressor combined by a hybrid neural network
(merging three different ANNs) are employed to predict
earthquakes in three different regions. Furthermore, it exists some
ANN approaches applied to precursors signals to predict
earthquakes. For instance, Moustra et al [20] predict the magnitude
of the impending seismic events using artificial neural networks
and the seismic electric signals as features for input data, because
they are believed to occur before an earthquake and considered as
earthquake precursors. Itai et al [13] introduce a multilayer neural
network using compression data to detect precursor signals from
the electromagnetic waves. These waves radiate from the earth’s
crust and they are useful for earthquake prediction. Külahci et al
[16] build A three-layer Levenberg-Marquardt feedforward
learning algorithm using eight different parameters including radon
gas changes for earthquake prediction. Ozerdem et al [23]
Elaborate a neural network model to extract correlations between
Spatio-temporal electric field data in order to detect hazard
precursory anomalous signal patterns.
Works based on deep learning i.e. Li and Liu [17] suggest a
combination of backpropagation neural networks and an improved
variant of particle swarm optimization for magnitude prediction,
for improving PSO they used a non-linear decreasing inertia weight
strategy. Mahmoudi et al [18] develop 128 different MLP networks
to find the best architecture of the magnitude earthquake prediction
model. Narayanakumar and Raja [21] use seismic indicators and
historical data, and evaluate the performance of Back-propagation
(BP) neural networks to predict earthquakes in the region of
Himalaya. The proposed model is a three-layer feed-forward BP
ANN. Finally, Panakkat and Adeli [24] propose an RNN model for
earthquake location and time prediction of moderate to large
earthquakes using seismic, considering two cases of studies
location decomposition and time decomposition.
We notice that most of the abovementioned neural network models
use various kinds of features as input to predict the time and
magnitudes of earthquakes, but none of them considers the
decomposition of magnitude ranges, and the correlations are not
well studied either.
3. MODEL ARCHITECTURE
3.1. Recurrent neural networks architecture
Recurrent neural networks (RNNs) are a class of artificial neural
networks (ANNs) specified by their memory state.
The RNNs network is similar to ANN's one, where the model
calculates the output by multiplying inputs with weights and the
activation function, in order to add the non-linearity to the network
(see Figure 2). In contrast, RNNs considers the memorized output
of the previous time step t-1 and add it to the inputs of current time
step t, and this is the role of their memory state (equation 1 and 2).
ht=tanh (Whhht-1+Wxhxt)
(1)
yt=Whyht
(2)
where Whh is the weight of the previous hidden state h t-1, xt is the
current input, Wxh is the weight of the current input state and tanh
is the activation function. yt is the output state and Why is the weight
at the output state.
The memory is the key of RNNs which allows them to learn the
correlations in sequence data, where it examines the whole context
and elements of each timestep to make predictions.
RNNs are applicable in time series data because they are dependent
on each other, which present behavior and the trend change by time
in their sequence values.
But, RNNs become sometimes untrainable since they suffer from
the vanishing and exploding gradient problem. When the
information is passing in long timesteps and deep layers, the
gradients can't progress and converge then the model cannot learn
and the gradient stays constant.
3.2. Long short-term memory architecture
LSTM is an RNN that replaces the standard neural network layer
with LSTM cells, proposed by Hochreiter [11] in 1991.
The LSTM cells are enhanced by three components called gates:
the input gate, the forget gate and the output gate, its architecture is
illustrated in Figure 2.
LSTM trains the features in a different fashion where it starts by
using the tanh activation function to squash the input data and make
them very small in a very non-linear manner. After that, the features
are passed into the input gate which takes the relevant information
from the squashed input by multiplying them with a sigmoid
function, this function filters the elements that are not required
where the values are between 0 (remove from the network) and 1
(pass through the network). Afterward, another important element
called the internal state is the memory of the current state. It takes
into account the previous state s t-1 and adds it to the input data (as
in RNN). It uses an addition operation instead of multiplication to
avoid the vanishing problem. These operations are described in the
following equations:
The recurrence of states is enforced by a forget gate, this gate
decides which state elements should be memorized or forgotten
using a sigmoid function.
Finally, the Tanh function squashes the outputs. These outputs are
controlled by an output gate that specifies the values that are
allowed to be the outputs of the current cell state.
it= σ (Wi . [ht-1, xt] + bi)
(3)
ct=tanh (Wc [ht-1, xt] + bc)
(4)
ft= σ (Wf . [ht-1, xt ] + bf)
(5)
ot= σ ( Wo [ht-1, xt] + bo)
(6)
ht=ot * tanh (ct)
(7)
where it, ct, ft, ot, ht are the input gate, cell state, forget gate, output
gate, and the hidden state respectively. Wi, Wc, Wf and Wo are their
weight matrices respectively. bi, bc, bf, and bo are the biases. Xt is
the input, ht-1 is the last hidden state, ht is the internal state. σ is the
sigmoid function.
4. LSTM MODEL FOR EARTHQUAKE
PREDICTION
In this section, we present the dataset studied in our work, the
preprocessing used techniques and finally we explain our
methodology and describe our LSTM models architecture.
4.1. Dataset
In this work, we use the dataset of seismic activity of the regions of
Morocco recorded from 1900 to 2019. The data was gathered from
the national geophysical institute of CNRST.
4.1.1.
Data preprocessing
After cleaning, removing duplicated data and merging data files
into one file, we set 10 features which present:
•
•
•
•
The geographic characteristics Latitude and
Longitude of the seismic event
The depth of the event by kilometers
Day, Month, Year, Hour, Minute and Second: we keep
all the elements of the time when the events occurred, to
conserve the exact information.
Mag is the magnitude of the seismic event
Some negative magnitudes presented as outliers in our dataset are
removed since they don’t cause any damage and they are not felt.
Feature generation or other possible added features are not treated,
since the ANN and deep learning models especially LSTM are
capable to extract and learn original and complex data without
using any other tool to generate their characteristics. For instance,
authors in [4] compared the situations when using the features with
generated characteristics like b-value and using original features,
thereby they found the same results which demonstrate the
capability of deep learning to learn insights by itself.
4.1.2.
Data description
After data preparation, we get 29689 from the 32396 seismic events
in which we will apply our model directly. Table 1 presents the
descriptive statistics of our data, where the largest magnitude is 7.3
and the smallest one is 0.02.
4.2. Methodology
Earthquakes events are a time series data, that are not captured by
linear and classical methods, but with LSTM complex architectures
can be successfully trained and predicted with multiple input
variables. Therefore, our research is based on this model in order to
find an efficient and performant result in earthquake prediction.
In this section, we present the important steps of our work to predict
earthquakes in Moroccan regions.
Initially, we start by introducing the first case of our work, which
uses the model LSTM and the whole dataset we have presented
above.
In the second case, we apply the LSTM model on decomposed data
based on their magnitude range. In the two cases of studies, we
predict the magnitude, location and year of the incoming
earthquakes.
The Flow chart in Figure 3 presents our proposed LSTM model to
predict earthquakes. In the first step, the datasets are normalized
using the Min-Max scaler, this scaler is a transformation technique
calculated by formula 8, it transforms datasets to an exact same
scale, in a range between 0 and 1. Such transformers are used to
standardize the features so that no one dominates the others. The
second step consists of dividing data into 80% of the training set
and 20% of the testing set. Then the model is trained and supported
by the mean squared error (MSE) for error calculation and
evaluation, and Adam optimizer for convergence to the minimum
error. Adam is widely used in deep learning because it helps the
model to achieve good results fast. In [15] empirical research
demonstrates that Adam works well and it outperforms other
stochastic optimizers. Finally, when the model is trained, the
predictions are computed on the testing set, and the gap between
predicted values and real values is calculated using the Mean
squared error and the Mean absolute error (MAE). The MAE and
MSE, are used to evaluate regression models and were used in
previous works of earthquakes prediction. Their calculations are
described in formula 9 and 10 respectively.
𝑥−min⁡(𝑥)
z = max(𝑥)−min(𝑥)
(8)
𝑁
1
𝑀𝐴𝐸 = 𝑁 ∑
| 𝑦𝑖 − 𝑦̂| (9)
𝑖=1
1
𝑁
1
𝑀𝑆𝐸 = 𝑁 ∑
𝑖=1
(𝑦𝑖 − 𝑦̂)2 (10)
1
4.2.1. Study case 1: Earthquake prediction using
LSTM
In this case, we are using the 29689 seismic events, that we present
in 4.1.2, where we apply our proposed architecture of the LSTM
algorithm illustrated in figure 4. The proposed architectures are
found after doing a tuning approach to search the most adequate
architecture. The way used to tune is suggested in [27], where they
recommend finding the balance between underfitting and
overfitting with an examination of training and testing loss.
As it is described in figure 4, our proposed architecture contains a
LSTM layer, a dropout function, a dense network, and reLu
activation function. The LSTM layer contains 15 neurons which
give the optimal result after trying multiple numbers for this case.
The dropout function is applied to LSTM layer. It is used to drop
out of the network some neurons, where it doesn't consider them
during the training process. This function helps the model to avoid
overfitting since it ignores the co-dependency between neurons
[28]. This limits the power of each neuron to deal with its
calculation for new inputs individually, and focus on historical
data.
The dense network is a fully connected neural network, that is
connected to the output neurons of the LSTM layer. And it is used
to give the desired targets predicted from the output of the pattern
of the LSTM layer. The dense network applies an activation
function for outputs calculation. In our case, we use the Rectified
Linear Function (reLu) as it is simple and performant. It returns the
main value if it is positive and 0 if it is null or negative. This
function is recommended by Goodfellow and Bengio [12] where
they say: ‘’ major algorithmic change that has greatly improved the
performance of feedforward networks was the replacement of
sigmoid hidden units with piecewise linear hidden units, such as
rectified linear units.”. The outputs of the model are presented in
four features the Magnitude, Longitude, Latitude and Year of the
coming earthquakes.
4.2.2. Study case 2: Earthquake prediction using
LSTM and magnitude decomposition
In this case, we consider two LSTM-based models applied on
divided datasets. The decomposition of our dataset is based on their
range of magnitudes since the characteristics of large earthquakes
couldn’t be related to the smallest ones. There is a big gap between
the two seismic magnitude ranges. Large, medium and small
earthquakes do not present the same problem; each one have his
own type of dangers, patterns, and typical features, e.g. we could
never study rich and poor people shopping activities in one model
because we think it is the same case of our seismic datasets.
Hence, we decompose the datasets into two different magnitude
ranges, as follows:
•
•
Small and medium earthquakes: From magnitudes 0.2 to
under 5.0;
Large earthquakes: magnitude 5.0 and above.
The flowcharts in Figure 4 present our used architectures for each
model, which we found after testing multiple different ones. The
LSTM layer contains 15 neurons for small and medium
earthquakes, and 10 neurons for large earthquakes. The activation
function applied in both models is reLu and a dropout function is
applied in both models.
The outputs of the models are presented in four features the
Magnitude, Longitude, Latitude and Year of the coming
earthquakes.
5. PERFORMANCE EVALUATION AND
DISCUSSION
In this section, we evaluate the performance of each model in both
cases of studies using the MAE and MSE metrics. First, we start by
evaluating the performance of earthquake prediction when using
the whole dataset in one model. After that, we evaluate the
performance of the prediction when decomposing the datasets into
two different magnitude ranges. Finally, we discuss and compare
the results of the two cases, and we evaluate their performance
against ANN models with the same architectures.
The simulation results of our model in the first case give good
results where the MAE is 0.075 and the MSE is 0.014 as shown in
table 3. But, in the second case when data is decomposed into two
groups results become much better and improved; for large
earthquake, the MSE is 0.11 and MAE is 0.027, for small
earthquakes, the MAE is 0.041 and the MSE is 0.0058 and the
overall error are calculated where the MAE is 0.042 and MSE is
0.0059 (Table 2). Training time in the second case is faster since it
ends in 6.27 seconds before the first case’s model. Hence, the
decomposition of datasets by magnitudes gives more performant
results, because the lack of datasets for large earthquakes makes
them hard to learn. But, the power of LSTM in learning
complicated data realizes the extraction of patterns from the few
datasets of large earthquakes, especially when learning them
individually. The fitting curves of our models are illustrated in
Figure 5, all the models are well fitted and not overfitted since we
use the dropout function.
Comparing with last works is very hard in the field of earthquake
prediction because of the use of different performance metrics and
different dataset, catalogs, and regions [8]. Consequently, to
evaluate the performance of our LSTM models we build ANN
models with same architectures for both cases, Table 3 shows the
results of each case. We choose the algorithm ANN since it is
widely used in literature as it is presented in Section 2. The LSTM
models are outperforming the ANN models in both cases.
Especially when predicting large earthquakes, ANN gives 0.30 for
MAE and 0.13 for MSE. ANN doesn’t show any difference in
performance when decomposing datasets since it wasn't able to
well predict large earthquakes as LSTM did. Whereas, the training
time for decomposed data is slow by 37.62 seconds.
In brief, the results of our experiments prove the performance and
effectiveness of our LSTM models in earthquakes prediction. In
addition, our models are not complicated by using seismic
indicators and generated features. The LSTM can learn patterns and
features from datasets by itself. Furthermore, our work fills the full
meaning of earthquakes prediction where it gives all the abovementioned important elements in section 1, the magnitude, location
and time. Then, we recommend to use and evaluate our models in
earthquake prediction with similar datasets and seismic activity.
6. CONCLUSION
In this paper, we have suggested a new model for earthquake
prediction using historical datasets. We have built two model
prediction architectures. The first is an LSTM model that we apply
on the dataset of Moroccan regions. It predicts year, location and
magnitude of earthquakes. The second one focuses on datasets
decomposition based on their range of magnitudes and applies two
LSTM models on the divided data. The decomposition we propose
is performant and efficient especially when predicting large
earthquakes. Experiment results of our LSTM models are described
and evaluated and compared with ANN models. The results
demonstrate that our proposed model achieves favorable
performance compared to others.
Figure 1. Typical architecture of Recurrent neural networks
algorithm
Figure 2. Typical architecture of Long-short term memory
algorithm
Figure 4. Flow charts of LSTM models used in earthquake
prediction. The model in the left is applicated when using all
datasets, and the other two models are applicated when using
decomposed datasets.
Figure 3. Flow chart of the proposed LSTM model
Figure 5. Plotting results of prediction models with and without
datasets decomposition
Table 1. Descriptive statistics of seismic dataset of Morocco from 1900 to 2019 after data preparation
Characte
ristics
count
Depth
Latitude
Longitude
Year
Month
Day
Hour
Minute
Second
Mag
29689
24.15938
1
29689
34.61972
3
29689
29689
29689
29689
29689
5.884166
15.900805
29.421368
29.106403
2.410389
25.23118
1.777117
25.008664
3.505600
9.048841
29689
11.4719
93
6.93243
9
29689
std
29689
2000.95
4865
20.5470
24
17.397553
17.608949
0.879518
min
0.100000
20.02000
-4251.00000
1901.00
1.000000
1.000000
0.00000
0.000000
0.000000
0.020000
25%
10.00000
33.52400
-6.550000
1991.00
3.000000
8.000000
5.00000
14.000000
14.000000
1.700000
50%
18.07185
3
35.25000
-4.100000
2009.00
6.000000
16.000000
12.0000
29.000000
29.000000
2.400000
75%
30.00000
35.66600
-3.676000
2016.00
9.000000
24.000000
17.0000
44.000000
44.000000
2.940540
max
675.0000
43.32000
7.648000
2019.00
12.000000
31.000000
23.0000
59.000000
59.000000
7.300000
mean
-5.384475
infrared cloud images. (Dec. 2015), 98150E.
Table 2. Experiment results of LSTM models, when using all
datasets and when using decomposed magnitude ranges using
the performance metrics MAE and MSE, and the elapsed time
during training process.
[7]
Florido, E. et al. 2015. Detecting precursory patterns to
enhance earthquake prediction in Chile. Computers and
Geosciences.
76,
(Mar.
2015),
112–120.
DOI:https://doi.org/10.1016/j.cageo.2014.12.002.
[8]
Galkina, A. and Grafeeva, N. 2019. Machine learning
methods for earthquake prediction: A survey. CEUR
Workshop Proceedings. 2372, (2019), 25–32.
[9]
Hayakawa, M. 2016. Earthquake prediction with
electromagnetic
phenomena.
AIP
Conference
Proceedings (Feb. 2016).
[10]
Hayakawa, M. et al. 2016. On the Precursory Abnormal
Animal Behavior and Electromagnetic Effects for the
Kobe Earthquake (M~6) on April 12, 2013. Open Journal
of Earthquake Research. 05, 03 (2016), 165–171.
DOI:https://doi.org/10.4236/ojer.2016.53013.
[11]
Hochreiter, J. 1991. DIPLOMARBEIT IM FACH
INFORMATIK Untersuchungen zu dynamischen
neuronalen Netzen.
[12]
Ian Goodfellow, Yoshua Bengio, A.C. 2017. The Deep
Learning Book. MIT Press. 521, 7553 (2017), 785.
DOI:https://doi.org/10.1016/B978-0-12-391420-0.09987X.
[13]
Itai, A. et al. 2005. Multi-layer neural network for
precursor signal detection in electromagnetic wave
observation applied to great earthquake prediction. (Sep.
2005), 31–31.
[14]
Kannan, S. 2014. Innovative Mathematical Model for
Earthquake Prediction. Engineering Failure Analysis. 41,
(2014),
890–895.
DOI:https://doi.org/10.1016/j.engfailanal.2013.10.016.
[15]
Allen, C.R. 1976. Responsibilities in earthquake
prediction. Bulletin of the Seismological Society of
America. 66, 6 (1976), 2069–2074.
Kingma, D.P. and Ba, J.L. 2015. Adam: A method for
stochastic optimization. 3rd International Conference on
Learning Representations, ICLR 2015 - Conference Track
Proceedings (2015).
[16]
Asim, K.M. et al. 2017. Earthquake magnitude prediction
in Hindukush region using machine learning techniques.
Natural Hazards. 85, 1 (Jan. 2017), 471–486.
DOI:https://doi.org/10.1007/s11069-016-2579-3.
Külahcı, F. et al. 2009. Artificial neural network model for
earthquake prediction with radon monitoring. Applied
Radiation and Isotopes. 67, 1 (Jan. 2009), 212–219.
DOI:https://doi.org/10.1016/J.APRADISO.2008.08.003.
[17]
Asim, K.M. et al. 2018. Seismic indicators based
earthquake predictor system using Genetic Programming
and AdaBoost classification. Soil Dynamics and
Earthquake Engineering. 111, (Aug. 2018), 1–7.
DOI:https://doi.org/10.1016/j.soildyn.2018.04.020.
Li, C. and Liu, X. 2016. An improved PSO-BP neural
network and its application to earthquake prediction.
Proceedings of the 28th Chinese Control and Decision
Conference, CCDC 2016 (Aug. 2016), 3434–3438.
[18]
Mahmoudi, J. et al. 2016. Predicting the Earthquake
Magnitude Using the Multilayer Perceptron Neural
Network with Two Hidden Layers. Civil Engineering
Journal.
2,
1
(Jan.
2016),
1–12.
DOI:https://doi.org/10.28991/cej-2016-00000008.
[19]
Mignan, A. and Broccardo, M. 2019. Neural Network
Applications in Earthquake Prediction (1994-2019):
Meta-Analytic Insight on their Limitations. September
(2019), 1–25.
Metrics
MAE
MSE
Training
time
ALL
Dataset
Range
[0.2, 0.5[
Range
[0.5,0.2[
0.075612
26
0.014995
616
86.63059
59224700
9
0.041774
616
0.005821
1996
0.11138
759
0.02728
5077
15.6931
1920000
0012
61.66931
41
Total for the
decomposed
datasets
0.042091154
0.005918795
77.362433300
00002
Table 3. Experiment results of ANN models, when using all
datasets and when using decomposed magnitude ranges using
the performance metrics MAE and MSE, and the elapsed time
during training process.
Metrics
MAE
MSE
Training
time
ALL
Dataset
Range
[0.2, 0.5[
Range
[0.5,0.2[
0.072536
81569956
802
0.012023
64744513
1583
0.081498
21554646
755
0.015362
80415918
4586
75.60912
97000000
1
0.30554
2783127
6546
0.13679
7647679
3874
5.79379
8300000
006
43.78028
88
Total for the
decomposed
datasets
0.0825543178
852341
0.0159352242
96990257
81.402928000
00002
7. REFERENCES
[1]
[2]
[3]
[4]
Bhatia, A. et al. 2018. Earthquake forecasting using
artificial neural networks. International Archives of the
Photogrammetry, Remote Sensing and Spatial
Information Sciences - ISPRS Archives. 42, 5 (2018), 823–
827. DOI:https://doi.org/10.5194/isprs-archives-XLII-5823-2018.
[5]
Boucouvalas, A.C. et al. 2015. Modified-Fibonacci-DualLucas method for earthquake prediction. Third
International Conference on Remote Sensing and
Geoinformation of the Environment (RSCy2015) (Jun.
2015), 95351A.
[20]
Moustra, M. et al. 2011. Artificial neural networks for
earthquake prediction using time series magnitude data or
Seismic Electric Signals. Expert Systems with
Applications. 38, 12 (Nov. 2011), 15032–15039.
DOI:https://doi.org/10.1016/j.eswa.2011.05.043.
[6]
Fan, J. et al. 2015. Research on earthquake prediction from
[21]
Narayanakumar, S. and Raja, K. 2016. A BP Artificial
Neural Network Model for Earthquake Magnitude
Prediction in Himalayas, India. Circuits and Systems. 07,
11
(2016),
3456–3468.
DOI:https://doi.org/10.4236/cs.2016.711294.
[26]
[22]
Ni, Y.Q. et al. 2006. Experimental investigation of seismic
damage identification using PCA-compressed frequency
response functions and neural networks. Journal of Sound
and Vibration. 290, 1–2 (Feb. 2006), 242–263.
DOI:https://doi.org/10.1016/j.jsv.2005.03.016.
Sitharam, A.S.T.G. and Haider, S.T. 2015. Probabilistic
models for forecasting earthquakes in the northeast region
of India. Bulletin of the Seismological Society of America.
105,
6
(Dec.
2015),
2910–2927.
DOI:https://doi.org/10.1785/0120140361.
[27]
[23]
Ozerdem, M.S. et al. 2006. Self-organized maps based
neural networks for detection of possible earthquake
precursory electric field patterns. Advances in
Engineering Software. 37, 4 (2006), 207–217.
DOI:https://doi.org/10.1016/j.advengsoft.2005.07.004.
Smith, L.N. 2016. a Disciplined Approach To Neural
Network Hyper-Parameters: Part 1. (2016), 1–21.
[28]
Srivastava, N. et al. 2014. Dropout: A Simple Way to
Prevent Neural Networks from Overfitting.
[29]
Su, Y.P. and Zhu, Q.J. 2009. Application of ANN to
prediction of earthquake influence. 2009 2nd
International Conference on Information and Computing
Science, ICIC 2009 (2009), 234–237.
[24]
Panakkat, A. and Adeli, H. 2009. Recurrent neural
network for approximate earthquake time and location
prediction using multiple seismicity indicators. ComputerAided Civil and Infrastructure Engineering. 24, 4 (2009),
280–292.
DOI:https://doi.org/10.1111/j.14678667.2009.00595.x.
[25]
Pasari, S. 2018. Stochastic modelling of earthquake
interoccurrence times in Northwest Himalaya and
View publication stats
adjoining regions. Geomatics, Natural Hazards and Risk.
9,
1
(Jan.
2018),
568–588.
DOI:https://doi.org/10.1080/19475705.2018.1466730.