Investigating the efficiency of machine learning algorithms in classifying pulse-like ground motions

Wani, Faisal Mehraj; Vemuri, Jayaprakash; Rajaram, Chenna; Reddy, K. S. K. Karthik

doi:10.1007/s10950-023-10168-2

Cited by 6 publications

(1 citation statement)

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“…In the context of magnitude forecasting, the application of GaussianNB, Random Forest and SVM has the potential to reveal hidden patterns and relationships in the data [17], [18]. By utilizing attributes such as earthquake location, depth and time, we hope to be able to predict magnitude categories with a higher degree of accuracy.…”

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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Section: Introductionmentioning

confidence: 99%