Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors

Soil erosion is a devastating land degradation process that needs to be spatially analyzed for identification of critical zones for sustainable management. Geospatial prediction through susceptibility analysis assesses the occurrence of soil erosion under a set of causative factors (CFs). Previous s...

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Main Authors:	Sholagberu, Abdulkadir Taofeeq, Muhammad Raza, Ul Mustafa, Khamaruzaman, Wan Yusof, Ahmad Mustafa, Hashim, Shah, Mumtaz Muhammad, M., Waris, Isa, Mohamed H.
Format:	Article
Language:	English
Published:	Pjoes 2019
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/26329/ http://umpir.ump.edu.my/id/eprint/26329/ http://umpir.ump.edu.my/id/eprint/26329/ http://umpir.ump.edu.my/id/eprint/26329/1/Multivariate%20Logistic%20Regression%20Model.pdf

id	ump-26329
recordtype	eprints
spelling	ump-263292019-11-06T02:43:09Z http://umpir.ump.edu.my/id/eprint/26329/ Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors Sholagberu, Abdulkadir Taofeeq Muhammad Raza, Ul Mustafa Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim Shah, Mumtaz Muhammad M., Waris Isa, Mohamed H. TA Engineering (General). Civil engineering (General) Soil erosion is a devastating land degradation process that needs to be spatially analyzed for identification of critical zones for sustainable management. Geospatial prediction through susceptibility analysis assesses the occurrence of soil erosion under a set of causative factors (CFs). Previous studies have considered majorly static CFs for susceptibility analysis, but neglect dynamic CFs. Thus, this study presents an evaluation of erosion susceptibility under the influence of both non-redundant static and dynamic CFs using multivariate logistic regression (MLR), remote sensing and geographic information system. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as static CFs, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity representing the dynamic CFs. These were parameterized to establish geospatial relationships with the occurrence of erosion. The results showed that length-slope had the highest positive impact on the occurrence of erosion, followed by lineament density. During the MLR classification process, predicted accuracies for the eroded and non-eroded locations were 89.1% and 83.6% respectively, with an overall prediction accuracy of 86.6%. The model’s performance was satisfactory, with 81.9% accuracy when validated using the area-under-curve method. The output map of this study will assist decision makers in sustainable watershed management to alleviate soil erosion. Pjoes 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26329/1/Multivariate%20Logistic%20Regression%20Model.pdf Sholagberu, Abdulkadir Taofeeq and Muhammad Raza, Ul Mustafa and Khamaruzaman, Wan Yusof and Ahmad Mustafa, Hashim and Shah, Mumtaz Muhammad and M., Waris and Isa, Mohamed H. (2019) Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors. Polish Journal of Environmental Studies, 28 (5). pp. 3419-3429. ISSN 1230-1485 https://doi.org/10.15244/pjoes/91943 https://doi.org/10.15244/pjoes/91943
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Sholagberu, Abdulkadir Taofeeq Muhammad Raza, Ul Mustafa Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim Shah, Mumtaz Muhammad M., Waris Isa, Mohamed H. Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors
description	Soil erosion is a devastating land degradation process that needs to be spatially analyzed for identification of critical zones for sustainable management. Geospatial prediction through susceptibility analysis assesses the occurrence of soil erosion under a set of causative factors (CFs). Previous studies have considered majorly static CFs for susceptibility analysis, but neglect dynamic CFs. Thus, this study presents an evaluation of erosion susceptibility under the influence of both non-redundant static and dynamic CFs using multivariate logistic regression (MLR), remote sensing and geographic information system. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as static CFs, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity representing the dynamic CFs. These were parameterized to establish geospatial relationships with the occurrence of erosion. The results showed that length-slope had the highest positive impact on the occurrence of erosion, followed by lineament density. During the MLR classification process, predicted accuracies for the eroded and non-eroded locations were 89.1% and 83.6% respectively, with an overall prediction accuracy of 86.6%. The model’s performance was satisfactory, with 81.9% accuracy when validated using the area-under-curve method. The output map of this study will assist decision makers in sustainable watershed management to alleviate soil erosion.
format	Article
author	Sholagberu, Abdulkadir Taofeeq Muhammad Raza, Ul Mustafa Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim Shah, Mumtaz Muhammad M., Waris Isa, Mohamed H.
author_facet	Sholagberu, Abdulkadir Taofeeq Muhammad Raza, Ul Mustafa Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim Shah, Mumtaz Muhammad M., Waris Isa, Mohamed H.
author_sort	Sholagberu, Abdulkadir Taofeeq
title	Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors
title_short	Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors
title_full	Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors
title_fullStr	Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors
title_full_unstemmed	Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors
title_sort	multivariate logistic regression model for soil erosion susceptibility assessment under static and dynamic causative factors
publisher	Pjoes
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/26329/ http://umpir.ump.edu.my/id/eprint/26329/ http://umpir.ump.edu.my/id/eprint/26329/ http://umpir.ump.edu.my/id/eprint/26329/1/Multivariate%20Logistic%20Regression%20Model.pdf
first_indexed	2023-09-18T22:40:55Z
last_indexed	2023-09-18T22:40:55Z
_version_	1777416909282607104

Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors

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