Open AccessEarthquake trend prediction using long short-term memory RNN
Author(s) -
Tanvi Bhandarkar,
K Vardaan,
Nikhil Satish,
Sharath Nittur Sridhar,
R. Sivakumar,
Snehasish Ghosh
Publication year - 2019
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v9i2.pp1304-1312
Subject(s) - recurrent neural network , long short term memory , artificial neural network , computer science , earthquake prediction , term (time) , sequence (biology) , time sequence , time series , artificial intelligence , long term prediction , machine learning , seismology , geology , telecommunications , physics , quantum mechanics , biology , genetics
The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.