Enhancing flood susceptibility mapping in Meghna River basin by introducing ensemble Naive Bayes with stacking algorithms
Abu Reza Md. Towfiqul Islam,
No information about this author
Md. Uzzal Mia,
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Nílson Augusto Villa Nova
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et al.
Geomatics Natural Hazards and Risk,
Journal Year:
2025,
Volume and Issue:
16(1)
Published: Feb. 13, 2025
Language: Английский
Pixel-Wise Feature Fusion in Gully Susceptibility: A Comparison of Feed-Forward Neural Networks and Ensemble (Voting, Stacking) Models
Journal of African Earth Sciences,
Journal Year:
2025,
Volume and Issue:
unknown, P. 105633 - 105633
Published: March 1, 2025
Language: Английский
Comprehensive Evaluation of Satellite-Based Rainfall Measurements Through Rain Gauge Validation Using Advanced Statistical Regression and Machine Learning Models by Using Python
K. Sumith
No information about this author
Water Resources Management,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 22, 2025
Language: Английский
Influence of Geomorphological Parameters on Flash Flood Susceptibility Analyzed using a Coupled Approach of HEC-HMS Model and Logistic Regression
Zhenyue Han,
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Fawen Li,
No information about this author
Chengshuai Liu
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et al.
Water Resources Management,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 27, 2025
Language: Английский
Investigation of Flood Hazard Susceptibility Using Various Distance Measures in Technique for Order Preference by Similarity to Ideal Solution
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(16), P. 7023 - 7023
Published: Aug. 10, 2024
In
the
present
study,
flood
hazard
susceptibility
maps
generated
using
various
distance
measures
in
Technique
for
Order
Preference
by
Similarity
to
an
Ideal
Solution
(TOPSIS)
were
analyzed.
Widely
applied
such
as
Euclidean,
Manhattan,
Chebyshev,
Jaccard,
and
Soergel
used
TOPSIS
generate
of
Gökırmak
sub-basin
located
Western
Black
Sea
Region,
Türkiye.
A
frequency
ratio
(FR)
weight
evidence
(WoE)
adapted
hybridize
nine
conditioning
factors
considered
this
study.
The
Receiver
Operating
Characteristic
(ROC)
analysis
Seed
Cell
Area
Index
(SCAI)
validation
testing
extracting
70%
30%
inventory
data
map
testing,
respectively.
When
Under
Curve
(AUC)
SCAI
values
examined,
it
was
found
that
Manhattan
metric
hybridized
with
FR
method
gave
best
prediction
results
AUC
0.904
0.942
training
Furthermore,
natural
break
give
predictions
classes.
So,
measure
could
be
preferred
Euclidean
mapping
studies.
Language: Английский