Estimation of the Source Parameters of the Flores Earthquake and Their Correlation to Aftershocks

Santosa, Bagus Jaya

doi:10.12962/joe.v5i1.5021

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Using the WCSS tool and the Elbow methodology, we ultimately divided the cluster into four clusters see Figure 8. According to Santosa [41], an approach to categorization will classify objects. Consequently, each categorization attribute in the new data will be pertinent [42], [43].…”

Section: Resultsmentioning

confidence: 99%

Forecast earthquake precursor in the Flores Sea

Jufriansah,

Anggraini,

Zulfakriza

et al. 2023

IJEECS

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<span>Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake prediction is an effort to reduce seismic disasters, this research is essential. By generating predictions, it can reduce the devastation caused by earthquakes. Using the support vector machine (SVM), hyperparameter support vector machine (HP-SVM), and particle swarm optimization support vector machine (PSO-SVM) algorithms, this study seeks to forecast the Flores Sea earthquake. According to the estimation results, the SVM algorithm’s evaluation value is less precise than that of the HP-SVM, especially the linear HP-SVM and HP-SVM Polynomial models. However, the HP-SVM RBF model’s accuracy rating is identical to that of the traditional SVM model. <br /> The improvement of the PSO-SVM model, which has the finest gamma position and a value of 9.</span>

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Section: Resultsmentioning

confidence: 99%

Forecast earthquake precursor in the Flores Sea

Jufriansah,

Anggraini,

Zulfakriza

et al. 2023

IJEECS

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“…Consequently, in an effort to safeguard civilization from these possible risks, understanding and forecasting earthquakes is crucial [2]. The Flores maritime, a maritime region affected by the Flores Back Arc Trust and susceptible to seismic activity, is one of the regions vulnerable to earthquakes [3]- [6]. The Flores Sea Earthquake Data Catalog is a valuable data source that keeps records of earthquakes that occurred in this region [7].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Magnitude Category Based on The Flores Sea Earthquake

Jufriansah,

Khusnani,

Saputra

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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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 pre-processing, feature selection, model training, forecast result evaluation, and performance analysis, this study is expected to contribute to the development of more accurate and effective earthquake forecasting methods. From these results we first obtain the result 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. 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 requires more time to compute. 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 nonlinear separation is one of its key advantages; nevertheless, the main drawback is that it is sensitive to hyperparameter adjustments.

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Estimation of the Source Parameters of the Flores Earthquake and Their Correlation to Aftershocks

Cited by 2 publications

References 2 publications

Forecast earthquake precursor in the Flores Sea

Forecast earthquake precursor in the Flores Sea

Forecasting the Magnitude Category Based on The Flores Sea Earthquake

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