A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

Abstract

Rockfall source identification is the most challenging task in rockfall hazard and risk assessment. This difficulty rises in the areas where there is a presence of other types of the landslide, such as shallow landslide and debris flow. The aim of this paper is to develop and test a hybrid model that can accurately identify the source areas. High-resolution light detection and ranging data (LiDAR) was employed to derive the digital terrain model (DTM), from which several conditioning factors were extracted. These conditioning factors were optimized utilizing the ant colony optimization (ACO). Different machine learning algorithms, namely, logistic regression (LR), random tree (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN), in addition to their ensemble models (stacking, bagging, and voting), were examined. This is based on the selected best subset of conditioning factors and inventory dataset. Stacking LR-RT (the best fit model) was then utilized to produce the probabilities of different landslide types. On the other hand, the Gaussian mixture model (GMM) was optimized and applied for automatically identifying the slope threshold of the occurrence of the different landslide types. In order to reduce the model sensitivity to the alteration in various conditioning factors and to improve the model computations performance, land use probability area was formed. The rockfall sources were identified by integrating the probability maps and the reclassified slope raster based on the GMM results. The accuracy assessment reveals that the developed hybrid model can identify the probable rockfall regions with an accuracy of 0.95 based on the validation dataset and 0.94 on based the training dataset. The slope thresholds calculated by GMM were found to be > 58°, 22°–58°, and 9°–22° for rockfall, shallow landslide, and debris flow, respectively. This indicates that the model can be generalized and replicated in different regions, and the proposed method can be applied in various landslides studies.