Predictive modeling and simulation techniques for landslide risk management

Abstract

Landslides are common natural disasters in the hilly areas, inflicting significant damage to both the human lives and economy. Unlike other severe disasters like floods, earthquakes etc. landslides do noticeably have a significant impact on the development initiatives. Landslides are regulated by different triggering events, which makes it impossible to forecast their exact mechanism. During the last decade, researchers have focused on using machine learning to forecast landslides. The aim of our project is to estimate the probability of landslides. In our project we will use a set of 8 features to train the model and forecast landslides. The acquired data was studied by data count ,correlation matrix and distribution of feature data.We will be analyzing the biggest landslides and find the main reasons responsible behind these landslides. The dataset will be used to test various machine learning algorithms and examine a variety of factors and visualizations.We will then examine the models to determine which performs well and have better prediction accuracy.