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Ensemble models based on radial basis function network for landslide susceptibility mapping.

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Ensemble learning models significantly improved landslide susceptibility prediction. The Bagging-RBFC model achieved 98% AUC, demonstrating its effectiveness for hazard assessment and risk reduction.

Keywords:
BaggingDaggingDecorateEnsemble modelingMachine learning

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Area of Science:

  • Geosciences
  • Computer Science
  • Environmental Science

Background:

  • Landslide susceptibility modeling is crucial for mitigating natural hazards.
  • Ensemble learning techniques offer enhanced predictive accuracy by integrating multiple models.
  • Traditional models often struggle with the complex interplay of factors influencing landslide occurrence.

Purpose of the Study:

  • To develop and evaluate ensemble learning models for landslide susceptibility prediction.
  • To compare the performance of different ensemble methods (Dagging, Bagging, Decorate) combined with a Radial Basis Function Classifier (RBFC).
  • To identify the most accurate model for landslide prediction in the Trung Khanh district, Vietnam.

Main Methods:

  • Development of ensemble models using Dagging, Bagging, and Decorate algorithms coupled with RBFC.
  • Utilized a geospatial database of 45 historical landslides and 13 influencing variables (topography, geology, land use, human activities).
  • Performance evaluation using Area Under the Receiver Operating Characteristic Curve (AUC) and other metrics (PPV, NPV, SST, SPF, ACC, RMSE).

Main Results:

  • The Bagging-RBFC model demonstrated superior performance with an AUC of 98%, PPV of 86%, NPV of 95%, SST of 95%, SPF of 87%, ACC of 91%, and RMSE of 0.297.
  • Bagging-RBFC outperformed Dagging-RBFC, Decorate-RBFC, and the single RBFC model in landslide susceptibility prediction.
  • The study confirmed the effectiveness of ensemble learning in enhancing landslide predictive capabilities.

Conclusions:

  • Ensemble learning techniques, particularly Bagging-RBFC, are highly effective for accurate landslide susceptibility modeling.
  • The developed models provide a reliable tool for hazard assessment in landslide-prone regions.
  • Implementing these models can aid in saving lives and reducing infrastructure damage globally.