Enhancing the accuracy of groundwater level prediction at different scales using spatio-temporal graph convolutional model
Earth Science Informatics,
Год журнала:
2025,
Номер
18(2)
Опубликована: Фев. 1, 2025
Язык: Английский
Projection of groundwater level fluctuations using deep learning and dynamic system response models in a drought affected area
Earth Science Informatics,
Год журнала:
2025,
Номер
18(1)
Опубликована: Янв. 1, 2025
Язык: Английский
Hydrogeological Insights: Assessing Groundwater in Trans-Yamuna Using Decision Making Method, Prayagraj, India
Iranian Journal of Science and Technology Transactions of Civil Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 12, 2025
Язык: Английский
Multi-Frequency SAR Polarimetry and Ground Penetrating Radar for Paleochannel Identification in the Thar Desert, India
Remote Sensing Applications Society and Environment,
Год журнала:
2025,
Номер
unknown, С. 101533 - 101533
Опубликована: Апрель 1, 2025
Язык: Английский
An improved equation for potential discharge estimation in groundwater basin delineated watershed
Results in Engineering,
Год журнала:
2024,
Номер
unknown, С. 103238 - 103238
Опубликована: Окт. 1, 2024
Язык: Английский
Hybrid Drought Forecasting Framework for Water‐Scarce Regions Based on Support Vector Machine and Precipitation Index
Hydrological Processes,
Год журнала:
2024,
Номер
38(12)
Опубликована: Дек. 1, 2024
ABSTRACT
Drought
is
a
natural
event
that
slowly
deteriorates
water
reserves.
This
study
aims
to
develop
machine
learning–based
computational
framework
for
monitoring
drought
status
in
water‐scarce
regions.
The
proposed
integrates
the
precipitation
index
(PI)
with
support
vector
models
forecast
occurrences
based
on
an
autoregressive
modelling
scheme.
Due
suitability
of
PI
analysis
arid
climates,
developed
hybrid
model
appropriate
regions
limited
rainfall.
used
historical
dataset
from
1958
2020
at
Kuwait
International
Airport,
City.
area
characterised
by
scarce
rainfall
and
vulnerable
severe
shortages
owing
resources.
Initially,
time‐series
datasets
were
examined
stationarity
validate
utility
model.
autocorrelation
function
test
was
significantly
associated
time
series
12‐
24‐month
drought‐monitoring
scales.
Predictive
forecasting
constructed
predict
up
3
months
advance.
Statistical
evaluation
metrics
assess
performance
results
showed
strong
association
between
observed
predicted
events,
coefficients
determination
(
R
2
)
ranging
0.865
0.925
provide
managers
efficient
reliable
tools
assist
preparing
management
plans.
provides
guidance
improving
resource
resilience
under
shortage
scenarios
other
climatic
applying
suitable
indices
conjunction
robust
data‐driven
models.
baseline
policymakers
worldwide
establish
sustainable
conservation
strategies
crucial
insights
disaster
preparation.
Язык: Английский
Uncertainty Assessment of Ensemble Base Machine Learning Modeling for Multi-step Ahead Forecasting of Dam Reservoir Inflows
Iranian Journal of Science and Technology Transactions of Civil Engineering,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 26, 2024
Язык: Английский
Emerging Trends and Technologies for Conservation and Sustainable Approach in Groundwater Management
Advances in environmental engineering and green technologies book series,
Год журнала:
2024,
Номер
unknown, С. 175 - 202
Опубликована: Дек. 6, 2024
Groundwater
is
a
natural
renewable
resource
vital
for
any
life
on
Earth.
management
of
emerging
concern
the
conservation
and
protection
this
resource.
With
advent
innovative
technologies,
managing
such
resources
become
easier
to
some
extent.
This
chapter
illustrates
advanced
their
contribution,
challenges
future
prospects
sustainable
groundwater.
AI
methods
have
widespread
in
decision-making
recent
years
are
accepted
globally
due
cost-effectiveness,
time-saving,
efficient
nature.
AI-driven
models
provide
precise
analytical
modelling,
real-time
monitoring,
data
integration
groundwater
management.
Innovative
can
detect
vulnerable
regions
that
prone
pollution
depletion
level
draw
attention
scientists,
local
people
policymakers
prompt
intervention.
Язык: Английский