Monte Carlo Analysis

Comprehensive assessment of machine learning approaches for landslide susceptibility modeling in Kentucky's diverse geological settings. This research focuses on developing robust, statistically validated models for landslide hazard prediction.

Research Focus:

• Detailed analysis of relationship between inventory and susceptibility results
• Thousands of models generated within sensitivity assessment framework
• Comprehensive evaluation of different machine learning algorithms (SVM, LR, NB, BT)
• Statistical validation using AUC and model performance metrics
• Regional adaptation for Appalachian terrain characteristics

The project employs multiple machine learning algorithms including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), and Boosted Trees (BT) to create the most accurate susceptibility models for Kentucky's unique geological conditions.