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Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

Forests, ISSN: 1999-4907, Vol: 15, Issue: 4
2024
  • 1
    Citations
  • 0
    Usage
  • 3
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
  • Captures
    3
  • Mentions
    2
    • Blog Mentions
      1
      • 1
    • News Mentions
      1
      • 1

Most Recent Blog

Forests, Vol. 15, Pages 678: Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

Forests, Vol. 15, Pages 678: Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model Forests doi: 10.3390/f15040678 Authors: Siyuan Ma

Most Recent News

New Findings from China Earthquake Administration Describe Advances in Algorithms (Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model)

2024 APR 30 (NewsRx) -- By a News Reporter-Staff News Editor at Math Daily News -- Investigators discuss new findings in algorithms. According to news

Article Description

Efficient analysis of non-susceptibility to landslides targets regions with minimal or zero landslide probability, thereby obviating the need to estimate the likelihood for low-susceptibility zones. This study assesses the effectiveness of the quantile non-linear (QNL) model in delineating the non-susceptibility of landslides in China through a topographic index. The topographic index encompassed slope angle and topographic relief, which are calculated using a 3 × 3 and 15 × 15 square cell moving window, respectively. Additionally, a global landslide susceptibility model established using a comprehensive global landslide database and fuzzy algorithm was employed for comparative analysis, providing a holistic evaluation of the QNL model’s accuracy. The results show that while the overall distribution of the two QNL models for non-susceptible landslide areas was roughly consistent, notable discrepancies were observed in localized regions, especially in the Southwest and Qinghai-Tibet geological environment areas where landslides are prone to occur. The applicability of the QNL model is significantly limited in these areas. In addition, the predicted results of the QNL_CHN model are closer to those based on the global landslide susceptibility model of the fuzzy algorithm. This study provides valuable insights to enhance the QNL model’s applicability, thereby strengthening forest ecosystem management and mitigating ecological disaster risks.

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