Print Email Facebook Twitter Earthquake Prediction: A MLP & SVM Comparison Title Earthquake Prediction: A MLP & SVM Comparison Author van den Akker, Daniel (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Isufi, E. (mentor) Yang, M. (mentor) Sabbaqi, M. (mentor) Tax, D.M.J. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-01-28 Abstract Multi-Layer Perceptron and Support Vector Machine have both been widely used in machine learning. In this research paper, these models have been applied to binary classification on an individual time series basis. The goal was to see whether they can predict earthquakes, using earthquakes measured at specific stations across New Zealand. As it turns out, both models serve as satisfactory classifiers. However, their performances are dependent on the stations the data was accumulated from. Subject Deep Neural NetworksMulti-layer PerceptronSupport Vector MachineSVMMLPEarthquake analsyisEarthquake predictionSeismic data processingMachine learning To reference this document use: http://resolver.tudelft.nl/uuid:59bb6c7b-3709-403e-b39f-0a5cb4b3f8a3 Part of collection Student theses Document type bachelor thesis Rights © 2022 Daniel van den Akker Files PDF final_bachelor_thesis_3.pdf 1.93 MB Close viewer /islandora/object/uuid:59bb6c7b-3709-403e-b39f-0a5cb4b3f8a3/datastream/OBJ/view