MODEL SUPPORT VEKTOR MACHINE (SVM) BERDASARKAN PARAMETER WINDOWS UNTUK PREDIKSI KEKUATAN GEMPA BUMI

Earthquakes are a type of natural disaster that currently cannot be predicted. Predicting the value of earthquake magnitude for related parties such as government and National Disaster Management Authority is very important. Furthermore, the results of earthquake predictions by several parties are used as indicators in post-earthquake response in minimizing the risks that will occur. Several studies have applied machine learning methods to predict earthquakes such as deep neural networks and parallel Support Vector Regression. In this article, we propose a data mining method using the Support Vector Machine (SVM) algorithm accompanied by the optimization of the windowing parameter value in the model that is applied to predict the value of the earthquake magnitude. Based on its advantages, the SVM model was chosen because it has been applicable in time series data processing. In the experimental stage process, parameter settings are first carried out, namely setting the kernel type, sampling type, and number of windowing to optimize the level of accuracy of the resulting model. The results showed that the best model with the smallest Root Mean Square Error (RMSE) was 0.712

Major earthquake event prediction using various machine learning algorithms

International audienceAt least two basic categories of earthquake prediction exist: short-term predictions and forecast ones. Short term earthquake predictions are made hours or days in advance, while forecasts are predicted months to years in advance. The majority of studies are done on forecast, taking into consideration the history of earthquakes in specific countries and areas. In this context, the core idea of thiswork is to predict whereas an event is classified as negative or positive major earthquake by applying different machine learningalgorithms. Eight different algorithms have been applied on a real earthquake dataset, namely: Random Forest, Naive Bayes, LogisticRegression, MultiLayer Perceptron, AdaBoost, K-nearest neighbors, Support Vector Machine, and Classification and Regression Trees. Foreach selected model, various hyperparameters have been selected, and obtained prediction results have been fairly compared using variousmetrics, leading to a reliable prediction of major events for 3 of them

Toward Predicting Global Seismicity of the Earth using Machine Learning Techniques and Solar Activity Data

An earthquake is one of the deadliest natural disasters. Forecasting an earthquake is a challenging task since natural causes such as movement of tectonic plates, volcanic eruptions, rainfall, and tidal stress all play an important part in earthquakes. Earthquakes can also be caused by human beings, such as mining, dams, nuclear bomb testing, etc. Solar activity has also been suggested as a possible cause of earthquakes. Solar activity and earthquakes occur in different parts of the solar system, on the Sun’s surface and the Earth’s surface, separated by a huge distance. However, scientists have been trying to figure out if there are any links between these two seemingly unrelated occurrences since the 19th century. In this study, four machine learning algorithms k-nearest neighbour, support vector regression, random forest regression, and Long Short-Term Memory network were applied to understand if there is a relationship between solar activity and earthquakes. The study employed three types of solar activity: sunspot number, solar wind, and solar flares, as well as worldwide earthquake frequencies that ranged in magnitude and depth. The study's findings imply that the Long Short-Term Memory network model predicts earthquakes more accurately than other models. There's a chance that earthquakes are influenced by solar activity. Earthquakes with a magnitude less than 5.5 are more linked to solar activity than earthquakes with a magnitude equal to or higher than 5.5. Solar activity has a bigger impact on earthquakes of lower depths