Utilization of Seismic Data as a Tsunami Vulnerability Review

Khusnani, Azmi; Jufriansah, Adi; Afriyanto, Mulya

doi:10.12928/irip.v5i2.6706

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The Flores back arc thrust is the cause of the Flores Sea's seismic activity [1], [2]. The Kalaotoa fault, which was discovered by the December 14, 2021, earthquake, is a new fault [3]- [5]. According to the Meteorological, Climatological, and Geophysical Agency (BMKG) study, East Nusa Tenggara will experience an increase in the number of earthquakes in 2022, with a total of 3,621 occurrences.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Forecast earthquake precursor in the Flores Sea

Jufriansah,

Anggraini,

Zulfakriza

et al. 2023

IJEECS

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“…Due to the potential for bodily harm and fatalities, earthquakes have long been a plague for people [1]. Consequently, in an effort to safeguard civilization from these possible risks, understanding and forecasting earthquakes is crucial [2].…”

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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Comparison of K-Means Algorithm and DBSCAN on Aftershock Activity in the Flores Sea: Seismic Activity 2019-2022

Aprianti,

Jufriansah,

Donuata

et al. 2023

JNEST

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This study seeks to determine whether the clustering method can be used to analyze Flores Sea earthquake activity. In this investigation, the BMKG Repo serves as the source for real earthquake vibration data collection. The stages of this research include preparing the data in CSV format and then preparing the data to eliminate useless data by identifying missing data. On the basis of the research data, it was determined that the K-Means and DBSCAN methods are used to determine the clustering method for analyzing earthquake activity. In addition, the data is depicted using a graphical Elbow method so that we can determine the number of clusters of aftershocks in the Flores Sea. The results of the visualization of aftershocks that followed earthquakes in the Flores Sea between 2019 and 2022 revealed three distinct groups of earthquake source depths: 33 to 70 kilometers, 150 to 300 kilometers, and 500 to 800 kilometers. In terms of the shilhoute index parameter, the K-Means algorithm is preferable to the DBSCAN algorithm when clustering results are used to analyze earthquake activity.

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Utilization of Seismic Data as a Tsunami Vulnerability Review

Cited by 4 publications

References 27 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

Comparison of K-Means Algorithm and DBSCAN on Aftershock Activity in the Flores Sea: Seismic Activity 2019-2022

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