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Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning

Krishnagopal Halder1,2, Amit Kumar Srivastava3,4, Anitabha Ghosh5

  • 1Department of Remote Sensing and GIS, Vidyasagar University, Vidyasagar University Rd, Midnapore, 721102, West Bengal, India. Krishnagopal.Halder@zalf.de.

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Summary

This study developed an advanced ensemble machine learning framework for accurate landslide susceptibility mapping in West Bengal, India. The Meta Classifier model demonstrated superior prediction, identifying high-risk zones for better disaster management.

Keywords:
LandslideMachine learningRecursive feature elimination, meta-learning framework

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Area of Science:

  • Geosciences and Remote Sensing
  • Environmental Science
  • Data Science and Machine Learning

Background:

  • Landslides present substantial risks to ecosystems, human lives, and economies, especially in geologically unstable regions like the Sub-Himalayan West Bengal.
  • Accurate landslide susceptibility prediction is crucial for effective disaster management and land-use planning in vulnerable areas.

Purpose of the Study:

  • To enhance landslide susceptibility prediction in West Bengal by developing an ensemble machine learning framework.
  • To identify key landslide-conditioning factors and classify susceptibility zones using advanced computational techniques.
  • To compare the performance of seven machine learning models for landslide prediction.

Main Methods:

  • An ensemble framework integrating Recursive Feature Elimination (RFE) with meta-learning techniques was developed.
  • Seven machine learning models (Logistic Regression, Support Vector Machine, Random Forest, Extremely Randomized Trees, Gradient Boosting, Extreme Gradient Boosting, and a Meta Classifier) were applied.
  • Remote Sensing and GIS tools were utilized for data processing and analysis, with model performance evaluated using accuracy, precision, recall, F1 score, and AUC.

Main Results:

  • The Meta Classifier (MC) achieved the highest accuracy (0.956) and AUC (0.987), outperforming individual models.
  • Gradient Boosting (GB), XGBoost, and Random Forest (RF) also showed strong performance with high accuracy and AUC values.
  • Extremely Randomized Trees (ET) demonstrated high accuracy (0.946) and AUC (0.985), with efficient feature selection capabilities.

Conclusions:

  • The developed ensemble framework, particularly the Meta Classifier, provides a robust and scalable method for landslide susceptibility mapping.
  • High and very high susceptibility zones were identified in Darjeeling and Kalimpong, influenced by rainfall, geology, and human activities.
  • The findings offer critical insights for land-use planning, disaster mitigation, and environmental conservation in hazard-prone regions globally.