Violin plot illustrating the comparison between observed and simulated Runoff.

<p>Violin plot illustrating the comparison between observed and simulated Runoff.</p>

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Main Author: Mohammed Majeed Hameed (14053535) (author)
Other Authors: Adil Masood (14053526) (author), Aadil hamid (21417847) (author), Ahmed Elbeltagi (10149420) (author), Siti Fatin Mohd Razali (14053529) (author), Ali Salem (2967900) (author)
Published: 2025
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_version_ 1852020057312329728
author Mohammed Majeed Hameed (14053535)
author2 Adil Masood (14053526)
Aadil hamid (21417847)
Ahmed Elbeltagi (10149420)
Siti Fatin Mohd Razali (14053529)
Ali Salem (2967900)
author2_role author
author
author
author
author
author_facet Mohammed Majeed Hameed (14053535)
Adil Masood (14053526)
Aadil hamid (21417847)
Ahmed Elbeltagi (10149420)
Siti Fatin Mohd Razali (14053529)
Ali Salem (2967900)
author_role author
dc.creator.none.fl_str_mv Mohammed Majeed Hameed (14053535)
Adil Masood (14053526)
Aadil hamid (21417847)
Ahmed Elbeltagi (10149420)
Siti Fatin Mohd Razali (14053529)
Ali Salem (2967900)
dc.date.none.fl_str_mv 2025-05-23T20:03:53Z
dc.identifier.none.fl_str_mv 10.1371/journal.pone.0321008.g009
dc.relation.none.fl_str_mv https://figshare.com/articles/figure/Violin_plot_illustrating_the_comparison_between_observed_and_simulated_Runoff_/29142028
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Medicine
Ecology
Science Policy
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
willmott index (<
support societies impacted
novel statistical method
inform sustainable practices
extreme gradient boosting
detecting turning points
complex hydrological processes
2016 – 2021
2002 – 2015
obtaining forecasting improvements
glacierized catchments affected
study also found
xgboost model achieves
forecasting monthly runoff
div >< p
ahead runoff forecasting
forecasting models
glacierized catchment
study ’
study focuses
runoff data
water management
term memory
significant autocorrelation
remaining data
random forest
providing insights
may hinder
lotschental catchment
long short
flood control
deep learning
climate change
best accuracy
554 m³
dc.title.none.fl_str_mv Violin plot illustrating the comparison between observed and simulated Runoff.
dc.type.none.fl_str_mv Image
Figure
info:eu-repo/semantics/publishedVersion
image
description <p>Violin plot illustrating the comparison between observed and simulated Runoff.</p>
eu_rights_str_mv openAccess
id Manara_3807cc866b8dfaad00a3d1486cc85ee4
identifier_str_mv 10.1371/journal.pone.0321008.g009
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/29142028
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Violin plot illustrating the comparison between observed and simulated Runoff.Mohammed Majeed Hameed (14053535)Adil Masood (14053526)Aadil hamid (21417847)Ahmed Elbeltagi (10149420)Siti Fatin Mohd Razali (14053529)Ali Salem (2967900)MedicineEcologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedwillmott index (<support societies impactednovel statistical methodinform sustainable practicesextreme gradient boostingdetecting turning pointscomplex hydrological processes2016 – 20212002 – 2015obtaining forecasting improvementsglacierized catchments affectedstudy also foundxgboost model achievesforecasting monthly runoffdiv >< pahead runoff forecastingforecasting modelsglacierized catchmentstudy ’study focusesrunoff datawater managementterm memorysignificant autocorrelationremaining datarandom forestproviding insightsmay hinderlotschental catchmentlong shortflood controldeep learningclimate changebest accuracy554 m³<p>Violin plot illustrating the comparison between observed and simulated Runoff.</p>2025-05-23T20:03:53ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0321008.g009https://figshare.com/articles/figure/Violin_plot_illustrating_the_comparison_between_observed_and_simulated_Runoff_/29142028CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291420282025-05-23T20:03:53Z
spellingShingle Violin plot illustrating the comparison between observed and simulated Runoff.
Mohammed Majeed Hameed (14053535)
Medicine
Ecology
Science Policy
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
willmott index (<
support societies impacted
novel statistical method
inform sustainable practices
extreme gradient boosting
detecting turning points
complex hydrological processes
2016 – 2021
2002 – 2015
obtaining forecasting improvements
glacierized catchments affected
study also found
xgboost model achieves
forecasting monthly runoff
div >< p
ahead runoff forecasting
forecasting models
glacierized catchment
study ’
study focuses
runoff data
water management
term memory
significant autocorrelation
remaining data
random forest
providing insights
may hinder
lotschental catchment
long short
flood control
deep learning
climate change
best accuracy
554 m³
status_str publishedVersion
title Violin plot illustrating the comparison between observed and simulated Runoff.
title_full Violin plot illustrating the comparison between observed and simulated Runoff.
title_fullStr Violin plot illustrating the comparison between observed and simulated Runoff.
title_full_unstemmed Violin plot illustrating the comparison between observed and simulated Runoff.
title_short Violin plot illustrating the comparison between observed and simulated Runoff.
title_sort Violin plot illustrating the comparison between observed and simulated Runoff.
topic Medicine
Ecology
Science Policy
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
willmott index (<
support societies impacted
novel statistical method
inform sustainable practices
extreme gradient boosting
detecting turning points
complex hydrological processes
2016 – 2021
2002 – 2015
obtaining forecasting improvements
glacierized catchments affected
study also found
xgboost model achieves
forecasting monthly runoff
div >< p
ahead runoff forecasting
forecasting models
glacierized catchment
study ’
study focuses
runoff data
water management
term memory
significant autocorrelation
remaining data
random forest
providing insights
may hinder
lotschental catchment
long short
flood control
deep learning
climate change
best accuracy
554 m³