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

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-rotati...

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Autores principales: Arabameri, Alireza, Sadhasivam, Nitheshnirmal, Turabieh, Hamza, Mafarja, Majdi, Rezaie, Fatemeh, Pal, Subodh Chandra, Santosh, M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862281/
https://www.ncbi.nlm.nih.gov/pubmed/33542340
http://dx.doi.org/10.1038/s41598-021-82527-3
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author Arabameri, Alireza
Sadhasivam, Nitheshnirmal
Turabieh, Hamza
Mafarja, Majdi
Rezaie, Fatemeh
Pal, Subodh Chandra
Santosh, M.
author_facet Arabameri, Alireza
Sadhasivam, Nitheshnirmal
Turabieh, Hamza
Mafarja, Majdi
Rezaie, Fatemeh
Pal, Subodh Chandra
Santosh, M.
author_sort Arabameri, Alireza
collection PubMed
description 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.
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spelling pubmed-78622812021-02-05 Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management Arabameri, Alireza Sadhasivam, Nitheshnirmal Turabieh, Hamza Mafarja, Majdi Rezaie, Fatemeh Pal, Subodh Chandra Santosh, M. Sci Rep Article 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. Nature Publishing Group UK 2021-02-04 /pmc/articles/PMC7862281/ /pubmed/33542340 http://dx.doi.org/10.1038/s41598-021-82527-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Arabameri, Alireza
Sadhasivam, Nitheshnirmal
Turabieh, Hamza
Mafarja, Majdi
Rezaie, Fatemeh
Pal, Subodh Chandra
Santosh, M.
Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862281/
https://www.ncbi.nlm.nih.gov/pubmed/33542340
http://dx.doi.org/10.1038/s41598-021-82527-3
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