LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process

The present study investigates a potential application of different resolution topographic data obtained from airborne LiDAR and an integrated ensemble weight-of-evidence and analytic hierarchy process (WoE–AHP) model to spatially predict slope failures. Previously failed slopes of the Pellizzano (Italy) were remotely mapped and divided into two subsets for training and testing purposes. 1, 2, 5, 10, 15, and 20 m topographic data were processed to extract nine terrain attributes identified as conditioning factors for landslides: slope degree, aspect, altitude, plan curvature, profile curvature, stream power index, topographic wetness index, sediment transport index, and topographic roughness index. Landslide (slope failure) susceptibility maps were produced using a single WoE (Model 1), an ensemble WoE–AHP model that used all conditioning factors (Model 2), and an ensemble WoE–AHP model that only used highly nominated conditioning factors (Model 3). The validation results proved the efficiency of high-resolution (≤ 5 m) topographic data and the ensemble model, particularly when all factors were used in the modeling process (Model 2). The average success rates and prediction rates for Model 2 that used ≤ 5 m resolution datasets were 84.26 and 82.78%, respectively. The finding presented in this paper can aid in planning more efficient LiDAR surveys and the handling of large datasets, and in gaining a better understanding of the nature of the predictive models.