Intensity-Duration-Frequency Curves and Precipitation Frequency Analysis for the Ogun-Osun River Basin Using Annual Maxima Approach
Опубликована: Янв. 1, 2025
Язык: Английский
Accounting for historical data uncertainty in flood frequency analysis: the Upper Rhine River
Journal of Hydrology,
Год журнала:
2025,
Номер
unknown, С. 133480 - 133480
Опубликована: Май 1, 2025
Язык: Английский
Improving the Consistency of Hydrologic Event Identification
Environmental Modelling & Software,
Год журнала:
2025,
Номер
unknown, С. 106521 - 106521
Опубликована: Май 1, 2025
Язык: Английский
Delineation of flood risk terrains and rainfall visualisation in the North Western part of Ghana
Modeling Earth Systems and Environment,
Год журнала:
2024,
Номер
10(3), С. 4567 - 4594
Опубликована: Май 16, 2024
Язык: Английский
Impact of catchment and climate attributes on flood generating processes and their effect on flood statistics
Journal of Hydrology,
Год журнала:
2024,
Номер
646, С. 132361 - 132361
Опубликована: Ноя. 16, 2024
Язык: Английский
Shifting cold regions streamflow regimes in North America affect flood frequency analysis
Hydrological Sciences Journal,
Год журнала:
2024,
Номер
70(1), С. 51 - 70
Опубликована: Окт. 28, 2024
Over
threshold
flood
events
in
seventy
years
of
data
from
202
reference
hydrometric
stations
Canada
and
the
United
States
were
separated
into
nival,
mixed,
pluvial
types.
While
Mann-Kendall
trend
tests
showed
few
significant
trends
magnitude,
changes
type
fraction
found
over
time,
with
annual
mean
temperature,
precipitation.
Nival
decreased
frequency
seventy-year
period
16%
sites,
while
mixed
increased
(5%,
12%).
These
indicate
a
shift
nival
towards
more
dominated
systems.
Fewer
analysis
against
four
climate
indices.
Flood
using
combined
distribution
approach
three
types
resulted
larger
magnitude
design
flow
estimates
(median
increase
20
–
30
%)
comparison
results
considering
to
be
single
population.
Язык: Английский
The Use of Recurrent Neural Networks (S-RNN, LSTM, GRU) For Flood Forecasting Based on Data Extracted from Classical Hydraulic Modeling
Modelling in Civil Environmental Engineering,
Год журнала:
2023,
Номер
18(3), С. 1 - 18
Опубликована: Сен. 1, 2023
Abstract
Floods
are
natural
disasters
that
have
a
significant
impact
on
everyday
human
life,
both
through
material
losses
and
loss
of
life.
In
the
context
climate
change,
these
events
may
be
more
frequent
or
dangerous.
For
real-time
flood
forecasting,
fast
methods
for
determining
hydrographs
along
watercourses
needed.
Classic
hydraulic
modeling
software
provides
satisfactory
results,
but
in
many
cases
calculation
time
can
high.
Another
approach,
different
from
classical
is
use
neural
networks
forecasting
hydrographs.
Thus,
present
study
aims
to
analyze
three
types
recurrent
networks,
including
SRNN,
RNN-LSTM,
RNN-GRU.
each
network
type,
flow
level
resulting
were
provided
as
input
training
data.
Using
deep
learning
environment,
based
previous
calibration
validation
2
historical
modeled.
The
obtained
extremely
close
those
recorded,
while
running
tens
times
smaller.
Язык: Английский