SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors

Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of...

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Main Authors:	Muhammad Raza, Ul Mustafa, Abdulkadir, Taofeeq Sholagber, Khamaruzaman, Wan Yusof, Ahmad Mustafa, Hashim, M., Waris, Muhammad, Shahbaz
Format:	Conference or Workshop Item
Language:	English
Published:	EDP Sciences 2018
Subjects:	HD28 Management. Industrial Management
Online Access:	http://umpir.ump.edu.my/id/eprint/22550/ http://umpir.ump.edu.my/id/eprint/22550/ http://umpir.ump.edu.my/id/eprint/22550/13/SVM-based%20geospatial%20prediction.pdf

id	ump-22550
recordtype	eprints
spelling	ump-225502019-01-03T06:13:49Z http://umpir.ump.edu.my/id/eprint/22550/ SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors Muhammad Raza, Ul Mustafa Abdulkadir, Taofeeq Sholagber Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim M., Waris Muhammad, Shahbaz HD28 Management. Industrial Management Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of some crucial conditioning factors (CFs) termed dynamic CFs whose impacts on the accuracy have not been investigated. Thus, this study evaluates erosion susceptibility under the influence of both non-redundant static and dynamic CFs using support vector machine (SVM), remote sensing and GIS. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as non-redundant static factors, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity as the dynamic factors. The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. Using area under the curve (AUC) and Cohen’s kappa index (k) as the validation criteria, the results showed that polynomial function had the highest performance followed by linear and radial basis function. However, sigmoid SVM underperformed having the lowest AUC and k values coupled with higher classification errors. The CFs’ weights were implemented for the development of soil erosion susceptibility map. The map would assist planners and decision makers in optimal land-use planning, prevention of soil erosion and its related hazards leading to sustainable watershed management. EDP Sciences 2018 Conference or Workshop Item PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/22550/13/SVM-based%20geospatial%20prediction.pdf Muhammad Raza, Ul Mustafa and Abdulkadir, Taofeeq Sholagber and Khamaruzaman, Wan Yusof and Ahmad Mustafa, Hashim and M., Waris and Muhammad, Shahbaz (2018) SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors. In: MATEC Web of Conferences: 4th International Conference On Civil, Offshore & Environmental Engineering (ICCOEE2018), 13-14 Ogos 2018 , Kuala Lumpur Convention Centre (KLCC), Malaysia. pp. 1-11., 203 (04004). ISSN 2261-236X https://doi.org/10.1051/matecconf/201820304004
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	HD28 Management. Industrial Management
spellingShingle	HD28 Management. Industrial Management Muhammad Raza, Ul Mustafa Abdulkadir, Taofeeq Sholagber Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim M., Waris Muhammad, Shahbaz SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors
description	Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of some crucial conditioning factors (CFs) termed dynamic CFs whose impacts on the accuracy have not been investigated. Thus, this study evaluates erosion susceptibility under the influence of both non-redundant static and dynamic CFs using support vector machine (SVM), remote sensing and GIS. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as non-redundant static factors, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity as the dynamic factors. The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. Using area under the curve (AUC) and Cohen’s kappa index (k) as the validation criteria, the results showed that polynomial function had the highest performance followed by linear and radial basis function. However, sigmoid SVM underperformed having the lowest AUC and k values coupled with higher classification errors. The CFs’ weights were implemented for the development of soil erosion susceptibility map. The map would assist planners and decision makers in optimal land-use planning, prevention of soil erosion and its related hazards leading to sustainable watershed management.
format	Conference or Workshop Item
author	Muhammad Raza, Ul Mustafa Abdulkadir, Taofeeq Sholagber Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim M., Waris Muhammad, Shahbaz
author_facet	Muhammad Raza, Ul Mustafa Abdulkadir, Taofeeq Sholagber Khamaruzaman, Wan Yusof Ahmad Mustafa, Hashim M., Waris Muhammad, Shahbaz
author_sort	Muhammad Raza, Ul Mustafa
title	SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors
title_short	SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors
title_full	SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors
title_fullStr	SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors
title_full_unstemmed	SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors
title_sort	svm-based geospatial prediction of soil erosion under static and dynamic conditioning factors
publisher	EDP Sciences
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22550/ http://umpir.ump.edu.my/id/eprint/22550/ http://umpir.ump.edu.my/id/eprint/22550/13/SVM-based%20geospatial%20prediction.pdf
first_indexed	2023-09-18T22:33:38Z
last_indexed	2023-09-18T22:33:38Z
_version_	1777416450271608832

SVM-based geospatial prediction of soil erosion under static and dynamic conditioning factors

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