Recent Papers

Landslides represent a significant hazard to both human life and infrastructure, particularly in areas that are vulnerable to extreme weather and unstable terrain. Accurately forecasting landslide events is crucial for risk reduction and preparedness. This paper investigates the use of machine learning (ML) techniques to predict landslides by analyzing key environmental factors such as latitude, longitude, soil moisture, and temperature. Various datasets from diverse regions were utilized to train and test several ML algorithms, including support vector machines, decision trees, and neural networks. The study focused on preprocessing methods like feature scaling and normalization to optimize model performance and prevent overfitting. Results indicate that soil moisture and temperature are critical factors influencing landslide risks, while geographic coordinates provide important regional context. This research highlights the potential of ML models for landslide forecasting, offering new opportunities for early detection and improved disaster response strategies. Future work will explore integrating other variables like rainfall and vegetation data to further refine prediction accuracy.

Copyright © 2025 Kamepalli Sriya Choudary. This is an open access article distributed under the Creative Commons Attribution License.