Landslide Prediction Data Processing Using Logistic Regression Algorithm

Landslides pose a significant threat to life and property. This research aims to develop a machine-learning model to predict landslide occurrences, focusing on logistic regression. Key factors such as inclination, vibration, humidity, and precipitation thresholds are considered to categorize areas into risk levels: safe, warning, alert, and danger. To address the challenges of real-world data acquisition, a simulated environment was employed to generate a comprehensive dataset. The developed model achieved an accuracy of 84% on the validation dataset, demonstrating its potential for accurate landslide prediction. This research contributes to the advancement of early warning systems and risk mitigation strategies for landslide-prone areas.