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SVM Classification of Etna Volcanic Tremor by NYIRANSABIMANA Carmel is a document available to read on EtoBox.

The document discusses the application of Support Vector Machine (SVM) for classifying volcanic tremor data from Etna, Italy, during various volcanic activity states. The SVM classifier achieved a high accuracy of 94.7% in distinguishing between pre-eruptive, lava fountain, eruptive, and post-eruptive phases using spectrogram-based features. The study highlights the effectiveness of SVM in handling complex classification problems with reduced overfitting compared to other methods.

Author

NYIRANSABIMANA Carmel

Language

EN