Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Convolutional Neural Networks help to predict location of
  Earthquakes   We examine the applicability of modern neural network architectures to the
midterm prediction of earthquakes. Our data-based classification model aims to
predict if an earthquake with the magnitude above a threshold takes place at a
given area of size $10 \times 10$ kilometers in $10$-$60$ days from a given
moment. Our deep neural network model has a recurrent part (LSTM) that accounts
for time dependencies between earthquakes and a convolutional part that
accounts for spatial dependencies. Obtained results show that neural
networks-based models beat baseline feature-based models that also account for
spatio-temporal dependencies between different earthquakes. For historical data
on Japan earthquakes our model predicts occurrence of an earthquake in $10$ to
$60$ days from a given moment with magnitude $M_c > 5$ with quality metrics ROC
AUC $0.975$ and PR AUC $0.0890$, making $1.18 \cdot 10^3$ correct predictions,
while missing $2.09 \cdot 10^3$ earthquakes and making $192 \cdot 10^3$ false
alarms. The baseline approach has similar ROC AUC $0.992$, number of correct
predictions $1.19 \cdot 10^3$, and missing $2.07 \cdot 10^3$ earthquakes, but
significantly worse PR AUC $0.00911$, and number of false alarms $1004 \cdot
10^3$.

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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th