Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting
Journal of Hydroinformatics,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 26, 2024
ABSTRACT
The
utilisation
of
modelling
tools
in
hydrology
has
been
effective
predicting
future
floods
by
analysing
historical
rainfall
and
inflow
data,
due
to
the
association
between
climate
change
flood
frequency.
This
study
utilised
a
dataset
monthly
for
Terengganu
River
Malaysia,
it
is
renowned
its
hydrological
patterns
that
exhibit
high
level
unpredictability.
evaluation
predictive
precision
effectiveness
Optimised
Decision
Tree
ODT
model,
along
with
RF
GBT
models,
this
involved
several
indicators.
These
indicators
included
correlation
coefficient,
mean
absolute
error,
percentage
relative
root
square
Nash-Sutcliffe
efficiency,
accuracy
rate.
research
results
indicated
models
performed
better
than
model
inflows.
as
well
showed
validation
average
accuracies
94%,
91%,
92%,
respectively.
R²
values
were
90.2%,
84.8%,
96.0%,
respectively,
NES
ranged
from
0.92
0.94.
have
greater
implications,
extending
beyond
forecasting
rates
encompass
other
hydro-meteorological
variables
depend
exclusively
on
input
data.
Язык: Английский
Neural Networks and Fuzzy Logic-Based Approaches for Precipitation Estimation: A Systematic Review
Ingeniería e Investigación,
Год журнала:
2025,
Номер
44(3), С. e108609 - e108609
Опубликована: Янв. 31, 2025
Precipitation
estimation
at
the
river
basin
level
is
essential
for
watershed
management,
analysis
of
extreme
events
and
weather
climate
dynamics,
hydrologic
modeling.
In
recent
years,
new
approaches
tools
such
as
artificial
intelligence
techniques
have
been
used
precipitation
estimation,
offering
advantages
over
traditional
methods.
Two
major
paradigms
are
neural
networks
fuzzy
logic
systems,
which
can
be
in
a
wide
variety
configurations,
including
hybrid
modular
models.
This
work
presents
literature
review
on
metaheuristic
models
based
signal
processes,
focusing
applications
these
estimation.
The
selection
comparison
criteria
were
model
type,
input
output
variables,
performance
metrics,
fields
application.
An
increase
number
this
type
studies
was
identified,
mainly
involving
network
models,
tend
to
get
more
sophisticated
according
availability
quality
training
data.
On
other
hand,
hybridize
with
There
still
challenges
related
prediction
spatial
temporal
resolution
micro-basin
levels,
but,
overall,
very
promising
analysis.
Язык: Английский
Design of an enhanced fuzzy neural network-based high-dimensional information decision-making model for supply chain management in intelligent warehouses
Fangyuan Tian,
D. H. Yuan
Kybernetes,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 22, 2025
Purpose
This
paper
aims
to
optimize
supply
chain
information
decision-making
systems
better
manage
complex,
high-dimensional
and
uncertain
through
the
integration
of
fuzzy
logic
neural
network
technology.
Design/methodology/approach
A
framework
based
on
reasoning
is
developed
address
empirical
issues
in
traditional
systems.
Subsequently,
an
innovative
radial
basis
function-dynamic
(RBF-DFNN)
model
constructed,
enhancing
system’s
capability
interpret
information.
retains
advantages
dynamic
networks
(DFNN)
while
introducing
anti-fuzzy
layer
optimizing
membership
function
T-paradigm
layers.
Findings
The
RBF-DFNN
leads
creation
a
for
chains.
Experimental
results
indicate
that
this
effectively
utilizes
K-medoids
clustering
algorithm
accurately
capture
characteristics
intrinsic
correlations
data.
Parameter
optimization
significantly
improves
model’s
performance,
with
root
mean
squared
error
(RMSE)
absolute
(MAE)
enhanced,
resulting
coefficients
determination
rising
from
95.6
97.8–99.1%
compared
STPF-AIMM
ANFIS
networks.
Originality/value
study
contributes
advancement
management
by
developing
highly
intelligent
refined
model,
intelligence
level
storage
promoting
more
sophisticated
operations.
Язык: Английский
A novel approach for precipitation modeling using artificial intelligence-based ensemble models
Desalination and Water Treatment,
Год журнала:
2024,
Номер
317, С. 100188 - 100188
Опубликована: Янв. 1, 2024
Precipitation
is
the
primary
component
of
hydrologic
water
cycle
and
its
accurate
prediction
plays
a
significant
role
for
planning,
management
design
hydraulic
structures.
The
objective
study
intended
to
explore
new
approach
increase
efficiency
precipitation
in
arid,
semiarid
humid
zones.
was
implemented
using
relevant
data
from
stations
Iraq
Nigeria.
Support
vector
regression
(SVR),
artificial
neural
network
(ANN)
adaptive
neuro-fuzzy
inference
system
models
(ANFIS)
were
applied
single
modeling
30
years
monthly
average
data.
Thereafter,
nonlinear
ensemble
2
linear
techniques
improve
reliability
models.
Error
measures
as
well
goodness
fit
measure
criteria
employed
assess
performance
potential
Based
on
results
this
work
it
found
that
could
accuracy
much
38%
validation
phase.
It
also
confirmed
intelligence-based
can
efficiently
be
improved
by
application
Язык: Английский
Monthly Rainfall Forecasting Using High Order Singh’s Fuzzy Time Series Based on Interval Ratio Methods: Case Study Semarang City, Indonesia
Asian Journal of Probability and Statistics,
Год журнала:
2024,
Номер
26(8), С. 71 - 88
Опубликована: Июль 23, 2024
Aims:
Sample:
To
determine
the
effectiveness
of
proposed
forecasting
method,
namely
Singh's
fuzzy
time
series
based
on
high
order
(third
order)
interval
ratios.
And
find
out
results
in
January
2022.
Study
Design:
Modification
Place
and
Duration
Study:
monthly
rainfall
data
for
Semarang
City
from
2017
to
December
2021.
Methodology:
The
method
by
researcher
is
Singh
This
research
uses
a
combination
Chen
series.
Applying
Chen's
section
determining
universe
discourse
()
fuzzification
which
includes
discourse,
partitions,
forming
Fuzzy
Logical
Relationships
Relationship
Groups.
Then
apply
part.
Finally,
calculate
Average
Forecasting
Error
Rate
(AFER)
test
performance.
In
part,
it
obtained
through
heuristic
approach
building
rules
obtain
better
have
an
effect
very
small
AFER
values.
step
partition,
this
ratio
aims
reflect
variations
historical
data.
Conclusion:
Based
calculation
value,
third
0.2422%.
It
can
be
said
that
ratios
2021
good.
forecast
2022
196.80
mm3
or
into
category
heavy
rain.
Язык: Английский
A performance and interpretability assessment of machine learning models for rainfall prediction in the Republic of Ireland
Decision Analytics Journal,
Год журнала:
2024,
Номер
12, С. 100515 - 100515
Опубликована: Авг. 24, 2024
Rainfall
prediction
significantly
impacts
agriculture,
water
reserves,
and
preparations
for
flooding
conditions.
This
research
examines
the
performance
interpretability
of
machine
learning
(ML)
models
rainfall
in
Republic
Ireland.
The
study
uses
a
brute
force
approach
Leave
One
Feature
Out
(LOFO)
methodology
to
evaluate
model
under
highly
correlated
variables.
Results
reveal
consistent
across
ML
algorithms,
with
average
Area
Under
Curve
Precision-Recall
(AUC-PR)
scores
ranging
from
0.987
1.000,
certain
features
such
as
atmospheric
pressure
soil
moisture
deficits
demonstrating
significant
influence
on
outcomes.SHapley
Additive
exPlanations
(SHAP)
values
provide
insights
into
feature
importance,
reaffirming
significance
prediction.
underscores
importance
selection
enhancing
accuracy
usability
Язык: Английский