Print Email Facebook Twitter Short-term Earthquake Prediction via Recurrent Neural Network Models Title Short-term Earthquake Prediction via Recurrent Neural Network Models: Comparison among vanilla RNN, LSTM and Bi-LSTM Author Du, XIANGYU (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 Earthquake prediction has raised many concerns nowadays, due to the massive loss caused by earthquakes, as well as the significance of accurate forecasting. Lots of trials have been investigated and experimented but few achieved satisfying results on short-term prediction (i.e., usually those earthquakes that will happen in three months). It is cardinal to detect strikes within a few minutes or hours in advance. In this paper, given thirty seconds of waveform signal before earthquakes happen, we compare the performances of three different recurrent neural networks, namely vanilla recurrent neural network (RNN), long short-term memory (LSTM) and bidirectional LSTM, on earthquake prediction. We choose recurrent neural networks because their inner structures take advantage of learning the temporal dependencies from time series sequence. Results show that LSTM has better performance predicting on unseen data than the other two networks. Subject Earthquake PredictionTime Series ModelRecurrent Neural NetworkLong Short-term MemoryBidirectional Long Short-term MemoryOver-fittingGrid Search To reference this document use: http://resolver.tudelft.nl/uuid:ccfcdc1e-bd7c-44cb-a834-5b6b651dc09e Part of collection Student theses Document type bachelor thesis Rights © 2022 XIANGYU Du Files PDF BSc._Research_Paper_Xiangyu_Du.pdf 5.02 MB PDF BSc._Research_Paper_Xiangyu_Du.pdf 5.04 MB Close viewer /islandora/object/uuid:ccfcdc1e-bd7c-44cb-a834-5b6b651dc09e/datastream/OBJ1/view