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Spatial Analysis and Prediction of Soil Erosion in a Complex Watershed of Cameron Highlands, Malaysia

  • Taofeeq Sholagberu Abdulkadir
  • Raza Ul Mustafa Muhammad
  • Olayinka Gafar Okeola
  • Wan Yusof Khamaruzaman
  • Bashir Adelodun
  • Saheed Adeniyi Aremu
Chapter
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Land degradation in the form of erosion is a serious geo-hazard threatening land and water resources sustainability. Its socioeconomic and ecological impacts necessitated its geospatial prediction via susceptibility analysis. Accuracy of susceptibility mapping depends largely on modeling techniques and causative factors (CFs) considered. The study implements scarcely used CFs and support vector machine (SVM) technique for geospatial prediction of soil erosion for low and peak rainfall cycles in a complex watershed of Cameron Highlands Malaysia. The CFs considered are non-redundant static (drainage density, length slope, lineament density, and erodibility) along with some dynamic (surface temperature, soil moisture index, rainfall erosivity, and vegetation index) CFs. Four kernel functions of SVM with optimized kernel parameters were used for spatial prediction of erosion for both rainfall cycles. The model performances were validated using the commonest evaluation criteria. The results indicated that polynomial SVM outperformed other models and was used in developing soil erosion susceptibility maps. The distribution pattern of erosion showed that majority of the area are “low and moderately” susceptible. The analysis shows that most of the highly susceptible locations are within the urban and agriculture land-use. The study provides information on erosional land forms that could be advanced to gullies. This medium-scale susceptibility map produced could be useful for sustainable watershed management for mitigating erosion.

Keywords

SVM Rainfall Dynamic factors Gully erosion Susceptibility 

Notes

Acknowledgment

This research was funded by Universiti Teknologi PETRONAS via 2016 URIF grant 0153AA-G04.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Taofeeq Sholagberu Abdulkadir
    • 1
    • 2
  • Raza Ul Mustafa Muhammad
    • 2
    • 3
  • Olayinka Gafar Okeola
    • 1
  • Wan Yusof Khamaruzaman
    • 2
  • Bashir Adelodun
    • 4
  • Saheed Adeniyi Aremu
    • 1
    • 5
  1. 1.Department of Water Resources and Environmental EngineeringUniversity of IlorinIlorinNigeria
  2. 2.Department of Civil and Environmental EngineeringUniversitiTeknologi PETRONASBandar Seri IskandarMalaysia
  3. 3.Centre for Urban Resource Sustainability, Institute of Self-Sustainable BuildingUniversitiTeknologi PETRONASSeri IskandarMalaysia
  4. 4.Department of Agricultural and Biosystems EngineeringUniversity of IlorinIlorinNigeria
  5. 5.Lower Niger River Basin Development AuthorityIlorinNigeria

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