Arjun Nair 

Landslides cause over a billion dollars per year in damages and the situation is only exacerbated by climate change. Landslide forecasting, as a result, is key for detecting these disasters. Traditional landslide prediction models often rely on localized data and extensive computational resources, limiting their applicability on a global scale and therefore have an accuracy rate of around 30%. Addressing this gap, our research leverages a dataset which uses an enhanced version of NASA’s Global Landslide Catalog. The dataset spans from 2010-2020, containing 10,100 landslide events with meteorological and geological factors preceding the event. After identifying key drivers of landslides and the minimum slope requirements for different lithologies, a Random Forest model was created using python to predict landslides based on their severity index. An 81% accuracy rate was found when trained and tested, proving to be much more effective when compared to traditional models. The model can now be used to provide a novel, data-driven perspective on landslide prediction allowing for a quicker evacuation of areas prone to landslides and providing enough time for countermeasures to be employed.