Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management

Abstract We introduce novel hybrid ensemble models in gully erosion susceptibility mapping (GESM) through a case study in the Bastam sedimentary plain of Northern Iran. Four new ensemble models including credal decision tree-bagging (CDT-BA), credal decision tree-dagging (CDT-DA), credal decision tr...

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Autores principales:	Alireza Arabameri, Nitheshnirmal Sadhasivam, Hamza Turabieh, Majdi Mafarja, Fatemeh Rezaie, Subodh Chandra Pal, M. Santosh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2b232a1b707744089733828da19af3ad
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spelling	oai:doaj.org-article:2b232a1b707744089733828da19af3ad2021-12-02T14:06:50ZCredal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management10.1038/s41598-021-82527-32045-2322https://doaj.org/article/2b232a1b707744089733828da19af3ad2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82527-3https://doaj.org/toc/2045-2322Abstract We introduce novel hybrid ensemble models in gully erosion susceptibility mapping (GESM) through a case study in the Bastam sedimentary plain of Northern Iran. Four new ensemble models including credal decision tree-bagging (CDT-BA), credal decision tree-dagging (CDT-DA), credal decision tree-rotation forest (CDT-RF), and credal decision tree-alternative decision tree (CDT-ADTree) are employed for mapping the gully erosion susceptibility (GES) with the help of 14 predictor factors and 293 gully locations. The relative significance of GECFs in modelling GES is assessed by random forest algorithm. Two cut-off-independent (area under success rate curve and area under predictor rate curve) and six cut-off-dependent metrics (accuracy, sensitivity, specificity, F-score, odd ratio and Cohen Kappa) were utilized based on both calibration as well as testing dataset. Drainage density, distance to road, rainfall and NDVI were found to be the most influencing predictor variables for GESM. The CDT-RF (AUSRC = 0.942, AUPRC = 0.945, accuracy = 0.869, specificity = 0.875, sensitivity = 0.864, RMSE = 0.488, F-score = 0.869 and Cohen’s Kappa = 0.305) was found to be the most robust model which showcased outstanding predictive accuracy in mapping GES. Our study shows that the GESM can be utilized for conserving soil resources and for controlling future gully erosion.Alireza ArabameriNitheshnirmal SadhasivamHamza TurabiehMajdi MafarjaFatemeh RezaieSubodh Chandra PalM. SantoshNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alireza Arabameri Nitheshnirmal Sadhasivam Hamza Turabieh Majdi Mafarja Fatemeh Rezaie Subodh Chandra Pal M. Santosh Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management
description	Abstract We introduce novel hybrid ensemble models in gully erosion susceptibility mapping (GESM) through a case study in the Bastam sedimentary plain of Northern Iran. Four new ensemble models including credal decision tree-bagging (CDT-BA), credal decision tree-dagging (CDT-DA), credal decision tree-rotation forest (CDT-RF), and credal decision tree-alternative decision tree (CDT-ADTree) are employed for mapping the gully erosion susceptibility (GES) with the help of 14 predictor factors and 293 gully locations. The relative significance of GECFs in modelling GES is assessed by random forest algorithm. Two cut-off-independent (area under success rate curve and area under predictor rate curve) and six cut-off-dependent metrics (accuracy, sensitivity, specificity, F-score, odd ratio and Cohen Kappa) were utilized based on both calibration as well as testing dataset. Drainage density, distance to road, rainfall and NDVI were found to be the most influencing predictor variables for GESM. The CDT-RF (AUSRC = 0.942, AUPRC = 0.945, accuracy = 0.869, specificity = 0.875, sensitivity = 0.864, RMSE = 0.488, F-score = 0.869 and Cohen’s Kappa = 0.305) was found to be the most robust model which showcased outstanding predictive accuracy in mapping GES. Our study shows that the GESM can be utilized for conserving soil resources and for controlling future gully erosion.
format	article
author	Alireza Arabameri Nitheshnirmal Sadhasivam Hamza Turabieh Majdi Mafarja Fatemeh Rezaie Subodh Chandra Pal M. Santosh
author_facet	Alireza Arabameri Nitheshnirmal Sadhasivam Hamza Turabieh Majdi Mafarja Fatemeh Rezaie Subodh Chandra Pal M. Santosh
author_sort	Alireza Arabameri
title	Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management
title_short	Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management
title_full	Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management
title_fullStr	Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management
title_full_unstemmed	Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management
title_sort	credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2b232a1b707744089733828da19af3ad
work_keys_str_mv	AT alirezaarabameri credaldecisiontreebasednovelensemblemodelsforspatialassessmentofgullyerosionandsustainablemanagement AT nitheshnirmalsadhasivam credaldecisiontreebasednovelensemblemodelsforspatialassessmentofgullyerosionandsustainablemanagement AT hamzaturabieh credaldecisiontreebasednovelensemblemodelsforspatialassessmentofgullyerosionandsustainablemanagement AT majdimafarja credaldecisiontreebasednovelensemblemodelsforspatialassessmentofgullyerosionandsustainablemanagement AT fatemehrezaie credaldecisiontreebasednovelensemblemodelsforspatialassessmentofgullyerosionandsustainablemanagement AT subodhchandrapal credaldecisiontreebasednovelensemblemodelsforspatialassessmentofgullyerosionandsustainablemanagement AT msantosh credaldecisiontreebasednovelensemblemodelsforspatialassessmentofgullyerosionandsustainablemanagement
_version_	1718391952432431104

Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management

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