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<p>Landslide susceptibility assessment has been a comprehensive tool for decision makers. However, the efficacy of susceptibility model depends on factor selection and the scientific trustworthiness of the results yielded is varying. This research was objectified to select the factors for model construction through an ensemble of genetic algorithm and Boruta algorithm. 1,888 landslides and 1,888 non-landslides points were collected and randomly split into 70:30 ratio for model training and validation purpose. Twenty selected environmental factors were utilized for model construction. Six advanced machine learning models, Sparse Partial Least Square, Bayesian Generalized Linear Model, Neural Network with Principal Component Analysis, Multivariate Adaptive Regression Spline, Boosted Decision Tree and Extreme Gradient Boosting, were used for susceptibility map preparation with their hyperparameters optimized through Particle Swarm Optimization. The models attained astounding prediction results with testing dataset having AUCROC score of 0.84, 0.85, 0.89, 0.89, 0.87, and 0.95 respectively. Following AUCROC, the model performances were validated through the Quality Sum Index (Q’s), which resulted highest quantification for XGBoost model (3.54), which proved the model excellence. The model’s discrimination capability was quantified through Kolmogorov-Smirnov (KS) statistics, which showed XGBoost as the most efficient model having a KS value of 95.8%, following which came the MARS model with KS value of 65.9%. Furthermore, the uncertainty of the model was computed and confidence map (CNFM) was generated for actual susceptibility map. The regional policy makers for disaster mitigation will be greatly benefitted from the findings of this research.</p>

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Swarm optimization based heterogeneous machine learning techniques for enhanced landslide susceptibility assessment with comprehensive uncertainty quantification

  • 期刊论文

  • 发表日期：

    2025年1月4日

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  • Sumon Dey,
  • Swarup Das

摘要

Landslide susceptibility assessment has been a comprehensive tool for decision makers. However, the efficacy of susceptibility model depends on factor selection and the scientific trustworthiness of the results yielded is varying. This research was objectified to select the factors for model construction through an ensemble of genetic algorithm and Boruta algorithm. 1,888 landslides and 1,888 non-landslides points were collected and randomly split into 70:30 ratio for model training and validation purpose. Twenty selected environmental factors were utilized for model construction. Six advanced machine learning models, Sparse Partial Least Square, Bayesian Generalized Linear Model, Neural Network with Principal Component Analysis, Multivariate Adaptive Regression Spline, Boosted Decision Tree and Extreme Gradient Boosting, were used for susceptibility map preparation with their hyperparameters optimized through Particle Swarm Optimization. The models attained astounding prediction results with testing dataset having AUCROC score of 0.84, 0.85, 0.89, 0.89, 0.87, and 0.95 respectively. Following AUCROC, the model performances were validated through the Quality Sum Index (Q’s), which resulted highest quantification for XGBoost model (3.54), which proved the model excellence. The model’s discrimination capability was quantified through Kolmogorov-Smirnov (KS) statistics, which showed XGBoost as the most efficient model having a KS value of 95.8%, following which came the MARS model with KS value of 65.9%. Furthermore, the uncertainty of the model was computed and confidence map (CNFM) was generated for actual susceptibility map. The regional policy makers for disaster mitigation will be greatly benefitted from the findings of this research.