Dimensionality Reduction for Visualization of Hydrogeophysical and Meteorological Recordings on a Landslide Zone

The frequency and intensity of devastating landslides have been increasing worldwide. Timely prediction of slope failure can save lives and protect property. Slope movement is a result of several meteorological and hydrogeophysical variables, such as temperature and moisture content, but this complex relationship is still not well understood. To predict and characterise a slope failure, multiple measurands are usually collected. Since these numerous variables in the predictor set may cause significant increase in complexity, it becomes necessary to use methods that determine the relative importance of measurands that contribute directly to slope failure. To this end, we investigate three methods of visualisation of the feature space and dimensionality reduction, namely Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE) and Linear Discriminant Analysis (LDA), to analyse a range of surface and subsurface measurements from multiple sensors focusing on five stages of slope movement and then make failure predictions using XGBoost regression by setting as predictors two most important components from the extracted features. The results clearly show that LDA better clusters the data points and distinguishes the five different stages of slope movement, including two failures during the period of study encompassing eight years.