Machine learning (ML) has increasingly been applied in landslide analysis due to its ability to process complex data patterns, recognize trends, and predict landslide occurrences. Here's how ML is utilized in landslide analysis:

• Data Processing and Feature Extraction: ML algorithms process various data types like satellite imagery, topographic maps, rainfall data, soil composition, and historical landslide records. Feature extraction from these diverse datasets helps identify crucial parameters affecting landslide susceptibility, such as slope gradient, soil type, vegetation cover, precipitation patterns, and land use. 

• Classification and Prediction: ML models classify terrain into susceptibility classes (low, moderate, high) or predict landslide occurrences based on input features. Algorithms like Random Forest, Support Vector Machines, Neural Networks, and Gradient Boosting are employed for classification and regression tasks.