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Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping.

Ataollah Shirzadi1, Karim Soliamani2, Mahmood Habibnejhad3

  • 1Department of Watershed Sciences Engineering, Faculty of Natural Resources, University of Agricultural Science and Natural Resources of Sari, Sari P.O. Box 48181-68984, Iran. a.shirzadi@uok.ac.ir.

Sensors (Basel, Switzerland)
|November 8, 2018
PubMed
Summary
This summary is machine-generated.

Novel machine learning models, including multiboost (MB) and random subspace (RS) with alternating decision trees (ADTree), show promise for predicting shallow landslides. These ensemble methods offer improved accuracy for spatial landslide susceptibility mapping.

Keywords:
GISIranalternating decision treelandslidemachine learning algorithms

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Area of Science:

  • Geosciences and Remote Sensing
  • Artificial Intelligence in Environmental Science

Background:

  • Shallow landslides pose significant risks in mountainous regions like Kurdistan Province, Iran.
  • Accurate spatial prediction of landslide susceptibility is crucial for effective land use planning and disaster mitigation.
  • Existing machine learning models require further refinement for improved prediction accuracy in complex geological settings.

Purpose of the Study:

  • To introduce and evaluate novel ensemble machine learning algorithms for shallow landslide spatial prediction.
  • To investigate the influence of varying sample sizes and raster resolutions on model performance.
  • To compare the efficacy of multiboost (MB), bagging (BA), rotation forest (RF), and random subspace (RS) ensemble methods integrated with alternating decision trees (ADTree).

Main Methods:

  • Development of ADTree-based ensemble models: MB-ADTree, BA-ADTree, RF-ADTree, and RS-ADTree.
  • Spatial prediction of shallow landslides in Bijar City, Kurdistan Province, Iran.
  • Evaluation using statistical measures and Area Under the Receiver Operating Characteristic Curve (AUROC) across different sample size splits (60%/40%, 70%/30%, 80%/20%, 90%/10%) and raster resolutions (10 m, 20 m).

Main Results:

  • The Random Subspace (RS) model demonstrated high performance with 10 m resolution and 60%/40% or 70%/30% sample sizes.
  • The Multiboost (MB) model achieved high accuracy with 20 m resolution and 80%/20% or 90%/10% sample sizes.
  • RS-ADTree and MB-ADTree ensemble models consistently outperformed the standalone ADTree model, with MB-ADTree achieving the highest AUC of 0.942 at 80%/20% sample size and 20 m resolution.

Conclusions:

  • The proposed MB-ADTree and RS-ADTree ensemble models are effective and promising tools for shallow landslide spatial prediction.
  • Model performance is sensitive to variations in sample size and raster resolution, highlighting the need for careful parameter selection.
  • These advanced machine learning approaches can significantly aid planners and decision-makers in managing landslide-prone areas.