Predicting Landslides with Machine Learning: A Data-Driven Approach

Routs are usual tragedies that can cause significant damage to the environment and pose a serious danger to humanoid lives and infrastructure. Early detection and identification of potential landslide-prone areas are crucial for disaster mitigation and preparedness efforts. This abstract out-lines a comprehensive approach to landslide identification utilizing machine learning techniques. In recent years, machine learning has emerged as a powerful tool for analyzing geospatial data and predicting geological hazards such as landslides. This research leverages a diverse range of data sources, including remote sensing imagery, topographical maps, rainfall records, and geological data, to develop a robust landslide identification model. The key components of the proposed methodology involve data preprocessing, feature engineering, and the application of various machine learning algorithms. Remote sensing data, such as satellite imagery and LiDAR data, are used to extract valuable terrain features and land cover information. Rainfall data are incorporated to assess the influence of precipitation on landslide occurrence. Geological data contribute to the understanding of local geological conditions. Several machines learning algorithms, including but not limited to decision trees, support vector machines, and neural networks, are employed to create predictive models. These models are trained on historical landslide data and validated against real-world cases. Cross-validation techniques are applied to ensure the model’s robustness and generalization capabilities.