Dimensionality reduction for visualization of hydrogeophysical and metereological 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.