Landslides,
Journal Year:
2023,
Volume and Issue:
20(9), P. 1853 - 1863
Published: May 27, 2023
Abstract
In
this
study,
a
new
paradigm
compared
to
traditional
numerical
approaches
solve
the
partial
differential
equation
(PDE)
that
governs
thermo-poro-mechanical
behavior
of
shear
band
deep-seated
landslides
is
presented.
particular,
paper
shows
projections
temperature
inside
as
proxy
estimate
catastrophic
failure
landslides.
A
deep
neural
network
trained
find
temperature,
by
using
loss
function
defined
underlying
PDE
and
field
data
three
To
validate
network,
we
have
applied
following
cases:
Vaiont,
Shuping,
Mud
Creek
The
results
show
that,
creating
training
with
synthetic
data,
landslide
can
be
reproduced
allows
forecast
basal
case
studies.
Hence,
providing
real-time
estimation
stability
landslide,
other
solutions
whose
study
has
calculated
individually
for
each
scenario.
Moreover,
offers
novel
procedure
design
architecture,
considering
stability,
accuracy,
over-fitting.
This
approach
could
useful
also
applications
beyond
