1 research outputs found
Predicting Landslides Using Locally Aligned Convolutional Neural Networks
Landslides, movement of soil and rock under the influence of gravity, are
common phenomena that cause significant human and economic losses every year.
Experts use heterogeneous features such as slope, elevation, land cover,
lithology, rock age, and rock family to predict landslides. To work with such
features, we adapted convolutional neural networks to consider relative spatial
information for the prediction task. Traditional filters in these networks
either have a fixed orientation or are rotationally invariant. Intuitively, the
filters should orient uphill, but there is not enough data to learn the concept
of uphill; instead, it can be provided as prior knowledge. We propose a model
called Locally Aligned Convolutional Neural Network, LACNN, that follows the
ground surface at multiple scales to predict possible landslide occurrence for
a single point. To validate our method, we created a standardized dataset of
georeferenced images consisting of the heterogeneous features as inputs, and
compared our method to several baselines, including linear regression, a neural
network, and a convolutional network, using log-likelihood error and Receiver
Operating Characteristic curves on the test set. Our model achieves 2-7%
improvement in terms of accuracy and 2-15% boost in terms of log likelihood
compared to the other proposed baselines.Comment: Published in IJCAI 202