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Multicollinearity and spatial correlation analysis of landslide conditioning factors in Langat River Basin, Selangor

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  • Siti Norsakinah Selamat

    (Universiti Kebangsaan Malaysia)

  • Nuriah Abd Majid

    (Universiti Kebangsaan Malaysia)

  • Mohd Raihan Taha

    (Universiti Kebangsaan Malaysia)

Abstract

Landslides are complex geological phenomena that occurred caused of one or more conditioning factors. It can be difficult to analyse the landslide occurrence phenomena and produce landslide susceptibility mapping. However, selecting an appropriate contributing factor for the landslide model will generate valuable landslide susceptibility mapping. This paper assesses the potentiality and suitability of landslide conditioning factors for evaluating relationship landslide spatial analysis. Ten landslides contributing factors including elevation, slope, aspect, curvature, Topography Wetness Index (TWI), lithology, soil series, distance to drainage, land use, and rainfall were selected Multicollinearity analysis. Next, to analyse the landslide spatial relationship between its conditioning factors, Geographical Information System (GIS) approach ware used. The results showed the most distribution landslides occurred on the elevation range from 13.62 to 463.90 m, the slope between 16º to 25º, northeast direction for aspect, convex surface for curvature, 11 to 15 index TWI, within 200 m distance to the river, acid intrusive for lithology, and Steep land for soil series. Langat River Basin substantial rainfall, exceeding 2100 mm annually, exacerbates slope instability and contributes significantly to landslide frequency. By doing this, the present study contributes to identified appropriate conditioning factors and it is very important in future studies to develop landslide susceptibility analysis at Langat River Basin. The insights presented are invaluable for policymakers, land use planners, and disaster management agencies in implementing proactive measures to reduce the impact of landslides and enhance the region’s resilience to geological hazards.

Suggested Citation

  • Siti Norsakinah Selamat & Nuriah Abd Majid & Mohd Raihan Taha, 2025. "Multicollinearity and spatial correlation analysis of landslide conditioning factors in Langat River Basin, Selangor," 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. 121(3), pages 2665-2684, February.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:3:d:10.1007_s11069-024-06903-8
    DOI: 10.1007/s11069-024-06903-8
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    References listed on IDEAS

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    1. Syaidatul Azwani Zulkafli & Nuriah Abd Majid & Ruslan Rainis, 2023. "Spatial Analysis on the Variances of Landslide Factors Using Geographically Weighted Logistic Regression in Penang Island, Malaysia," Sustainability, MDPI, vol. 15(1), pages 1-26, January.
    2. Tharshini Murthy & Izham Mohamad Yusoff & Siti Hamsah Samsudin & Taksiah A. Majid & Ismail Ahmad Abir & Chan Huah Yong & Mohd Ashraf Mohamad Ismail & Mohd Ashraf Mohamad Ismail, 2023. "Geographical Information System (GIS)-Based Landslide Susceptibility Mapping in Malaysia: A Review of Past, Current and Future Trends," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 12, May.
    3. Amit Bera & Bhabani Prasad Mukhopadhyay & Debasish Das, 2019. "Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: a case study from Eastern Himalayas, Namchi, South Sikkim," 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. 96(2), pages 935-959, March.
    4. Wei Xie & Wen Nie & Pooya Saffari & Luis F. Robledo & Pierre-Yves Descote & Wenbin Jian, 2021. "Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China," 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. 109(1), pages 931-948, October.
    5. Jie Dou & Hiromitsu Yamagishi & Hamid Pourghasemi & Ali Yunus & Xuan Song & Yueren Xu & Zhongfan Zhu, 2015. "An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan," 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. 78(3), pages 1749-1776, September.
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