In
this
paper,
an
integrated
mapping
of
the
georeferenced
data
is
presented
using
QGIS
and
GMT
scripting
tool
set.
The
study
area
encompasses
Bolivian
Andes,
South
America,
notable
for
complex
geophysical
geological
parameters
high
seismicity.
A
integration
was
performed
a
detailed
analysis
setting.
included
raster
vector
datasets
captured
from
open
sources:
IRIS
seismic
(2015
to
2021),
satellite-derived
gravity
grids
based
on
CryoSat,
topographic
GEBCO
data,
geoid
undulation
EGM-2008,
georeferences’
USGS.
techniques
processing
quantitative
qualitative
evaluation
seismicity
setting
in
Bolivia.
result
includes
series
thematic
maps
Andes.
Based
analysis,
western
region
identified
as
most
seismically
endangered
Bolivia
with
risk
earthquake
hazards
Cordillera
Occidental,
followed
by
Altiplano
Real.
magnitude
here
ranges
1.8
7.6.
shows
tight
correlation
between
gravity,
geophysics,
topography
cartographic
scripts
used
are
available
author’s
public
GitHub
repository
open-access
provided
link.
utility
combined
GIS
spatial
supported
automated
mapping,
which
has
applicability
assessment
hazard
America.
International Journal of Information Management Data Insights,
Год журнала:
2024,
Номер
4(1), С. 100210 - 100210
Опубликована: Янв. 4, 2024
Water
quality
and
its
management
are
the
most
precise
concerns
confronting
humanity
globally.
This
article
evaluates
various
sensors
used
for
water
monitoring
focuses
on
index
considering
multiple
physical,
chemical,
biological
parameters.
A
Review
of
Internet
Things
(IoT)
research
analysis,
can
help
remote
parameters
using
IoT-based
that
convey
assembled
estimations
utilizing
Low-Power
Wide
Area
Network
innovations.
Overall,
IoT
system
was
95
%
accurate
in
measuring
pH,
Turbidity,
TDS,
Temperature,
while
traditional
method
only
85
accurate.
Also,
this
study
reviewed
different
A.I.
techniques
to
assess
quality,
including
conventional
machine
learning
techniques,
Support
Vector
Machines,
Deep
Neural
Networks,
K-nearest
neighbors.
Compared
methods,
deep
significantly
increase
accuracy
measurements
groundwater
quality.
However,
variables,
such
as
caliber
training
data,
metrics'
complexity,
frequency,
will
affect
accuracy.
The
geographical
information
(GIS)
is
spatial
data
analysis
managing
resources.
also
paper.
Based
these
analyses,
has
forecasted
future
sensors,
Geospatial
Technology,
analysis.
Journal of Hydrology Regional Studies,
Год журнала:
2024,
Номер
52, С. 101703 - 101703
Опубликована: Фев. 12, 2024
A
pilot
case
study
in
East
El
Oweinat
(PCSEO),
Egypt.
An
artificial
neural
network
(ANN)-based
mountain
gazelle
optimization
(MGO)
model
was
applied
to
map
groundwater
potential
zones
(GWPZs).
For
this
purpose,
ten
layers
affecting
occurrence
were
prepared
and
normalized
against
the
drawdown
(DD)
map.
All
data
divided
into
70:30
for
training
testing.
After
that,
sensitivity
analysis
adopted
verify
relative
importance
(RI)
of
layers.
The
accuracy
GWPZs
checked
using
receiver
operating
characteristic
(ROC)
curve
other
statistical
indicators.
finally
propose
a
sustainable
strategy
exploration
by
implementing
integrated
MODFLOW-USG
MGO
framework.
Over
40%
PCSEO
revealed
high
very
degrees
situated
mostly
on
southwestern
side.
Sensitivity
that
significantly
affected
table
(GWT),
well
density
(WD),
land
use
(LU).
results
also
indicated
ANN-based
performed
with
an
area
under
(AUC)
∼
90%
compared
conventional
models.
Additionally,
MODFLOW-USG-based
gave
spatial
distribution
optimal
discharge
well-depth
zones.
This
finding
could
match
SDGs
relevant
ending
poverty,
affordable
groundwater,
life
land.
Journal of Hydrology Regional Studies,
Год журнала:
2022,
Номер
39, С. 100990 - 100990
Опубликована: Янв. 12, 2022
The
study
area
was
the
Anseong-si
that
located
in
southernmost
part
of
Gyeonggi-do
Province
at
127°19′
E,
36°82′
N.
Anseong
has
a
transitional
climate
between
north
and
south
regions.
Its
is
characterized
by
geographical
conditions
forming
expansive
plains
stretch
from
Charyeong
Range.
entire
city
surrounded
many
high
low
mountains
to
west,
late-middle
age
old
hills
are
spread,
while
there
due
development
rivers.
In
this
study,
machine
learning
algorithms
were
used
based
on
convolutional
neural
network
(CNN)
long
short-term
memory
(LSTM)
generate
groundwater
potential
map
Anseong,
South
Korea.
A
total
295
wells
locations
divided
median
value
transmissivity
data
(T)
produced
"1"
point
with
productivity
"0"
as
into
70:30
for
training
validation
model.
14
related
factors
such
topo-hydrological
geo-environmental
define
spatial
correlation
data.
model
evaluated
using
receiver
operating
characteristics
(ROC)
curve
analysis
method.
under
ROC
(AUC)
calculated
test
results
shows
good
accuracy,
AUC
values
all
higher
than
0.8.
Finally,
maps
generated
CNN
LSTM
can
be
analyze
areas
potentially
provided
This
could
help
local
environment
manage
resources
assist
planners
decision-makers
sustainability
planning.
Water,
Год журнала:
2023,
Номер
15(3), С. 419 - 419
Опубликована: Янв. 19, 2023
Groundwater
is
an
essential
source
of
water
especially
in
arid
and
semi-arid
regions
the
world.
The
demand
for
due
to
exponential
increase
population
has
created
stresses
on
available
groundwater
resources.
Further,
climate
change
affected
quantity
globally.
Many
parts
Indian
cities
are
experiencing
scarcity.
Thus,
assessment
potential
necessary
sustainable
utilization
management
We
utilized
a
novel
ensemble
approach
using
artificial
neural
network
multi-layer
perceptron
(ANN-MLP),
random
forest
(RF),
M5
prime
(M5P)
support
vector
machine
regression
(SMOReg)
models
assessing
Parbhani
district
Maharashtra
India.
Ten
site-specific
influencing
factors,
elevation,
slope,
aspect,
drainage
density,
rainfall,
table
depth,
lineament
land
use
cover,
geomorphology,
soil
types,
were
integrated
preparation
zones.
results
revealed
that
largest
area
was
found
under
moderate
category
GWP
zone
followed
by
poor,
good,
very
good
poor.
Spatial
distribution
zones
showed
Poor
GWPZs
spread
over
north,
central
southern
district.
Very
poor
mostly
north-western
study
calls
policy
implications
conserve
manage
these
parts.
ensembled
model
proved
be
effective
outcome
may
help
stakeholders
efficiently
utilize
devise
suitable
strategies
its
management.
Other
geographical
find
methodology
adopted
this
assessment.
Water,
Год журнала:
2023,
Номер
15(6), С. 1182 - 1182
Опубликована: Март 18, 2023
A
water
supply
is
vital
for
preserving
usual
human
living
standards,
industrial
development,
and
agricultural
growth.
Scarce
supplies
unplanned
urbanization
are
the
primary
impediments
to
results
in
dry
environments.
Locating
suitable
sites
artificial
groundwater
recharge
(AGR)
could
be
a
strategic
priority
countries
groundwater.
Recent
advances
machine
learning
(ML)
techniques
provide
valuable
tools
producing
an
AGR
site
suitability
map
(AGRSSM).
This
research
developed
ML
algorithm
identify
most
appropriate
location
Iranshahr,
one
of
major
districts
East
Iran
characterized
by
severe
drought
excessive
consumption.
The
area’s
undue
reliance
on
resources
has
resulted
aquifer
depletion
socioeconomic
problems.
Nine
digitized
georeferenced
data
layers
have
been
considered
preparing
AGRSSM,
including
precipitation,
slope,
geology,
unsaturated
zone
thickness,
land
use,
distance
from
main
rivers,
quality,
transmissivity
soil.
AGRSSM
was
trained
validated
using
1000
randomly
selected
points
across
study
area
with
accuracy
97%.
By
comparing
proposed
those
other
methods,
it
discovered
that
intelligence
method
accurately
determine
sites.
In
summary,
this
uses
novel
approach
optimal
algorithms.
Our
findings
practical
implications
policymakers
resource
managers
looking
address
problem
Iranshahr
regions
facing
similar
challenges.
Future
explore
applicability
our
examine
potential
economic
benefits