BIO Web of Conferences,
Год журнала:
2024,
Номер
144, С. 03005 - 03005
Опубликована: Янв. 1, 2024
This
study
develops
a
flow
direction
prediction
model
using
Sentinel-1
satellite
imagery
during
rainy
and
dry
seasons
through
the
Random
Forest
machine
learning
algorithm.
The
pre-processing
stage
includes
radiometric
calibration,
terrain
flattening,
speckle
filtering,
Doppler
correction.
processed
DEM
data
is
used
to
extract
key
topographic
parameters:
elevation,
slope,
curvature,
which
are
then
utilized
in
model.
built
with
500
trees
(n.trees),
mtry
of
2
for
season
3
season,
out-of-bag
(OOB)
error
estimates
8.76%
9.32%,
respectively.
Model
evaluation,
conducted
confusion
matrix,
reveals
high
performance,
average
Overall
Accuracy,
Kappa
User
Sensitivity,
Specificity
all
at
0.98
or
above.
analysis
shows
that
predominantly
shifts
northeast
(16.48%),
while
it
northwest
(16.85%).
Slope
significantly
influences
direction,
feature
importance
scores
60.76%
63.53%
season.
crucial
as
dictates
speed
water
under
gravity.
could
contribute
geothermal
field
management
by
accurately
predicting
surface
flow,
enhancing
monitoring,
promoting
sustainable
resource
management.
Journal of Environmental Engineering and Science,
Год журнала:
2025,
Номер
unknown, С. 1 - 12
Опубликована: Апрель 18, 2025
In
this
paper,
four
cities
in
India
having
different
geographical
structures,
climates,
and
hydrological
components
were
selected.
An
area
of
500
m
2
from
each
city
has
been
studied.
A
mathematical
model
with
three-dimensional
hydraulic
conductivity,
head
the
aquifer,
other
factors
was
adopted,
which
additional
artificial
recharge
added.
The
is
numerically
solved
help
C++
software,
details
provided
Appendix,
graphs
plotted
to
depict
status
groundwater
level
after
recharge.
It
found
that
during
study
period
2014
2021,
increase
Bhopal
9
m,
for
Jaipur
6
Dehradun
6.5
Bangalore
5
m.
Water Air & Soil Pollution,
Год журнала:
2025,
Номер
236(8)
Опубликована: Июнь 2, 2025
Abstract
This
study
investigates
toxic
metal
contamination
in
the
sediments
of
Değirmendere
River
Basin
(riverbed
and
estuary)
Türkiye,
with
aim
assessing
pollution
levels,
ecological
risks,
seasonal
variability
concentrations.
Sediment
samples
were
collected
from
20
stations
during
summer
winter
2022
analyzed
using
geochemical
statistical
methods.
The
metals
evaluated
include
Cu,
Pb,
Zn,
Ni,
As,
Co,
V,
La.
findings
reveal
that
Cu
(79.46
±
20.17
μg
g
-1
)
Pb
(63.83
15.11
exceeded
thresholds,
highest
concentrations
observed
winter,
particularly
estuarine
areas
affected
by
industrial
urban
activities.
Seasonal
variations
significant
for
Zn
(Mann-Whitney
U,
p
<
0.05),
higher
likely
due
to
increased
runoff
erosion.
Geoaccumulation
Index
(I
geo
Enrichment
Factor
(SEF)
indicated
moderate
As.
risk
index
(RI)
categorized
estuary
as
a
considerable
area,
RI
values
ranging
150
319.2.
These
highlight
anthropogenic
impacts
on
sediment
quality
Basin,
emphasizing
urgent
need
continuous
monitoring
targeted
mitigation
measures.
