Forecasting the Magnitude Category Based on The Flores Sea Earthquake
Dublin Core
Title
Forecasting the Magnitude Category Based on The Flores Sea Earthquake
Subject
gaussiannb; random forest; support vector machine; earthquake; forecasting
Description
Earthquakes are a phenomenon that is still a mystery in terms of predicting events, one of which is the magnitude. As technology
develops, there are many algorithms that can be used as approaches in earthquake forecasting. In the context of magnitude
forecasting, the application of GaussianNB, Random Forest and SVM has the potential to reveal these patterns and
relationships in the data. With the six main phases of this research, namely data acquisition, data preprocessing, feature
selection, model training, forecasting result evaluation, and performance analysis. From these results we obtain, firstly that
the GaussianNB model has a relatively simple and fast method in training its model. However, the weakness lies in the
assumption of a Gaussian distribution which may not always suit the complex and diverse characteristics of earthquake data.
Based on GaussianNB model, the model accurately predicts magnitude category 1 for 421 observations and magnitude
category 2 for 33 observations. Meanwhile, the magnitude 3 and magnitude 4 categories did not produce accurate predictions
from the model. Second, Random Forest, this method can increase accuracy and overcome the overfitting problem that occurs
when forecasting magnitudes. In contrast to GaussianNB, it tends to result in models with greater complexity and require more
time to compute. In our findings, we obtained an MSE value of 0.12 with an R2
score of -0.10, this indicates conditions that are
less effective in explaining differences in test data. The third option is SVM, which has both benefits and drawbacks that must
be taken into account. The capacity of SVM to separate data that has both linear and non-linear separation is one of its key
advantages; nevertheless, the main drawback is that it is sensitive to hyperparameter adjustments. It is clear from the results
of the algorithm comparison that SVM has more potential for earthquake forecasting, especially the linear SVM and polynomial
SVM model. The accuracy of the standard SVM is 0.587, which indicates relatively low performance. Linear SVM obtained a
very high accuracy of 0.998. Meanwhile, Polynomial SVM achieves perfect accuracy of 1.0. while RBF SVM has the same
accuracy as standard SVM, namely 0.587.
develops, there are many algorithms that can be used as approaches in earthquake forecasting. In the context of magnitude
forecasting, the application of GaussianNB, Random Forest and SVM has the potential to reveal these patterns and
relationships in the data. With the six main phases of this research, namely data acquisition, data preprocessing, feature
selection, model training, forecasting result evaluation, and performance analysis. From these results we obtain, firstly that
the GaussianNB model has a relatively simple and fast method in training its model. However, the weakness lies in the
assumption of a Gaussian distribution which may not always suit the complex and diverse characteristics of earthquake data.
Based on GaussianNB model, the model accurately predicts magnitude category 1 for 421 observations and magnitude
category 2 for 33 observations. Meanwhile, the magnitude 3 and magnitude 4 categories did not produce accurate predictions
from the model. Second, Random Forest, this method can increase accuracy and overcome the overfitting problem that occurs
when forecasting magnitudes. In contrast to GaussianNB, it tends to result in models with greater complexity and require more
time to compute. In our findings, we obtained an MSE value of 0.12 with an R2
score of -0.10, this indicates conditions that are
less effective in explaining differences in test data. The third option is SVM, which has both benefits and drawbacks that must
be taken into account. The capacity of SVM to separate data that has both linear and non-linear separation is one of its key
advantages; nevertheless, the main drawback is that it is sensitive to hyperparameter adjustments. It is clear from the results
of the algorithm comparison that SVM has more potential for earthquake forecasting, especially the linear SVM and polynomial
SVM model. The accuracy of the standard SVM is 0.587, which indicates relatively low performance. Linear SVM obtained a
very high accuracy of 0.998. Meanwhile, Polynomial SVM achieves perfect accuracy of 1.0. while RBF SVM has the same
accuracy as standard SVM, namely 0.587.
Creator
Adi Jufriansah, Azmi Khusnani, Sabarudin Saputra, Dedi Suwandi Wahab
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
December 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Adi Jufriansah, Azmi Khusnani, Sabarudin Saputra, Dedi Suwandi Wahab, “Forecasting the Magnitude Category Based on The Flores Sea Earthquake,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10128.