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Landslide Susceptibility Evaluation Using Different Slope Units Based on BP Neural Network.

Jianling Huang1, Xiaoye Zeng1, Lu Ding2

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Selecting the optimal slope unit scale is crucial for accurate landslide susceptibility mapping. This study found that 56,570 slope units provided the highest accuracy using a back propagation neural network, aiding hazard mitigation efforts.

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Area of Science:

  • Geosciences
  • Natural Hazards
  • Geographic Information Systems

Background:

  • Landslides pose significant risks to property and life annually.
  • Effective landslide susceptibility evaluation is vital for hazard assessment and loss mitigation.
  • The choice of mapping unit is a critical factor in landslide susceptibility studies.

Purpose of the Study:

  • To evaluate the impact of different slope unit scales on landslide susceptibility mapping.
  • To develop a landslide susceptibility map for Qingchuan County using the back propagation neural network technique.
  • To identify the most accurate slope unit scale for landslide susceptibility assessment.

Main Methods:

  • Utilized a dataset of 973 historical landslides and six conditioning factors (elevation, slope, aspect, lithology, distance to faults, distance to drainage).
  • Employed the back propagation (BP) neural network algorithm for susceptibility modeling.
  • Compared six different slope unit scales (4,401 to 69,013) using receiver operating characteristic (ROC) curves and area under the curve (AUC) for validation.

Main Results:

  • The landslide susceptibility map generated using 56,570 slope units achieved the highest accuracy, with an AUC of 0.9424.
  • Both overly detailed and overly generalized slope unit divisions resulted in suboptimal evaluation outcomes.
  • The study highlights the necessity of optimizing slope unit scale for precise landslide susceptibility assessment.

Conclusions:

  • The optimal scale of slope units is essential for accurate landslide susceptibility evaluation.
  • The back propagation neural network is an effective technique for landslide susceptibility mapping.
  • Findings provide valuable insights for developing effective landslide hazard mitigation strategies.