Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High Temporal Resolution Rainfall Runoff Modelling Using
  Long-Short-Term-Memory (LSTM) Networks   Accurate and efficient models for rainfall runoff (RR) simulations are
crucial for flood risk management. Most rainfall models in use today are
process-driven; i.e. they solve either simplified empirical formulas or some
variation of the St. Venant (shallow water) equations. With the development of
machine-learning techniques, we may now be able to emulate rainfall models
using, for example, neural networks. In this study, a data-driven RR model
using a sequence-to-sequence Long-short-Term-Memory (LSTM) network was
constructed. The model was tested for a watershed in Houston, TX, known for
severe flood events. The LSTM network's capability in learning long-term
dependencies between the input and output of the network allowed modeling RR
with high resolution in time (15 minutes). Using 10-years precipitation from
153 rainfall gages and river channel discharge data (more than 5.3 million data
points), and by designing several numerical tests the developed model
performance in predicting river discharge was tested. The model results were
also compared with the output of a process-driven model Gridded Surface
Subsurface Hydrologic Analysis (GSSHA). Moreover, physical consistency of the
LSTM model was explored. The model results showed that the LSTM model was able
to efficiently predict discharge and achieve good model performance. When
compared to GSSHA, the data-driven model was more efficient and robust in terms
of prediction and calibration. Interestingly, the performance of the LSTM model
improved (test Nash-Sutcliffe model efficiency from 0.666 to 0.942) when a
selected subset of rainfall gages based on the model performance, were used as
input instead of all rainfall gages.

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The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: econ
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
9
The categories of this paper are: nlin