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Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement

Author

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  • Okoli Jude Emeka

    (Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Haslinda Nahazanan

    (Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Bahareh Kalantar

    (RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan)

  • Zailani Khuzaimah

    (Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Ojogbane Success Sani

    (Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

Abstract

A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the water runoff of a slope. This paper introduces vegetation establishment as a low-cost, practical measure for slope reinforcement through the ground cover and the root of the vegetation. This study reveals the level of complexity of the terrain with regards to the evaluation of high and low stability areas and has produced a landslide susceptibility map. For this purpose, 12 conditioning factors, namely slope, aspect, elevation, curvature, hill shade, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distances to roads, distance to lakes, distance to trees, and build-up, were used through the analytic hierarchy process (AHP) model to produce landslide susceptibility map. Receiver operating characteristics (ROC) was used for validation of the results. The area under the curve (AUC) values obtained from the ROC method for the AHP model was 0.865. Four seed samples, namely ryegrass, rye corn, signal grass, and couch, were hydroseeded to determine the vegetation root and ground cover’s effectiveness on stabilization and reinforcement on a high-risk susceptible 65° slope between August and December 2020. The observed monthly vegetation root of couch grass gave the most acceptable result. With a spreading and creeping vegetation ground cover characteristic, ryegrass showed the most acceptable monthly result for vegetation ground cover effectiveness. The findings suggest that the selection of couch species over other species is justified based on landslide control benefits.

Suggested Citation

  • Okoli Jude Emeka & Haslinda Nahazanan & Bahareh Kalantar & Zailani Khuzaimah & Ojogbane Success Sani, 2021. "Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement," Land, MDPI, vol. 10(10), pages 1-23, September.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:10:p:995-:d:640222
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    References listed on IDEAS

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    1. Maher Maalouf, 2011. "Logistic regression in data analysis: an overview," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(3), pages 281-299.
    2. H. A. Nefeslioglu & E. Sezer & C. Gokceoglu & A. S. Bozkir & T. Y. Duman, 2010. "Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey," Mathematical Problems in Engineering, Hindawi, vol. 2010, pages 1-15, February.
    3. Siyuan Wang & Minmin Zhao & Xingmin Meng & Guan Chen & Runqiang Zeng & Qiang Yang & Yi Liu & Biao Wang, 2020. "Evaluation of the Effects of Forest on Slope Stability and Its Implications for Forest Management: A Case Study of Bailong River Basin, China," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    4. Thomas Stanley & Dalia B. Kirschbaum, 2017. "A heuristic approach to global landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 145-164, May.
    5. Emmanouil Psomiadis & Andreas Papazachariou & Konstantinos X. Soulis & Despoina-Simoni Alexiou & Ioannis Charalampopoulos, 2020. "Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data," Land, MDPI, vol. 9(5), pages 1-26, April.
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    Cited by:

    1. Ehtesham Mehmood & Imtiaz Rashid & Farooq Ahmed & Khalid Farooq & Akbar Tufail & Ahmed M. Ebid, 2022. "Hydrogeotechnical Predictive Approach for Rockfall Mountain Hazard Using Elastic Modulus and Peak Shear Stress at Soil–Rock Interface in Dry and Wet Phases at KKH Pakistan," Sustainability, MDPI, vol. 14(24), pages 1-15, December.
    2. Enrico Miccadei & Cristiano Carabella & Giorgio Paglia, 2022. "Landslide Hazard and Environment Risk Assessment," Land, MDPI, vol. 11(3), pages 1-5, March.

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