The Value of Terrain Pattern, High-Resolution Data and Ensemble Modeling for Landslide Susceptibility Prediction

Landslide risk is traditionally predicted by process-based models with detailed assessments or point-scale attribute-based machine learning (ML) models with first- or second-order features (e.g., slope and curvature) as inputs. One could hypothesize that terrain patterns might contain useful higher-order information that could be extracted, via computer vision ML models, to elevate prediction performance beyond that achievable with attribute-based models. We put this hypothesis to the test in the state of Oregon, where a large landslide data set is available. A Convolutional Neural Network (CNN) using 2D geospatial and terrain data (CNN2D) reached state-of-the-art single-model scores for Precision (0.90) and Recall (0.86), along with other metrics. CNN2D's Precision-Recall Pareto front, formed by applying different hyperparameters, dominated attribute-based models like Random Forest (RF1D) by a substantial margin, attesting to the value of fine-scale terrain patterns. However, CNN2D's superiority required high-resolution rainfall (∼800 m) and terrain (∼10 m) data sets: as the resolution coarsened, all models declined in performance but CNN2D's scores decreased more than RF1D's. Ensembling CNN2D and RF1D produced even better Recall (0.90), and this cross-model-type ensemble was also better than other ensembles. These models further showed robust results in cross-regional validation. Rainfall, land cover, and elevation were the most important predictors, while prescribed Plan and Profile Curvature fields were also highly useful inputs (perhaps due to the size of the training data set). Based on the results of our analyses, we generated landslide susceptibility maps which provide insights into spatial patterns of landslide risk.