Methods for Shallow Landslides Susceptibility Mapping: An Example in Oltrepo Pavese
The principal aim of this study is to compare different landslide susceptibility zonation models for predicting areas prone to shallow landsliding using both physically distributed landslide models and artificial neural networks. Necessary geotechnical and hydrological parameters were obtained coupling sample laboratory analysis and in situ measures; soil thickness was estimated using an empirical model while distribution of rainfall intensity was analyzed by performing a spatial interpolation. The predictive capabilities of these models were finally evaluated using a threshold-independent quantitative method (the ROC plot).
This paper investigates the use of reinforcement learning for the navigation of an over-actuated marine platform in unknown environments. The proposed approach uses an online least-squared policy iteration scheme for value function approximation in order to estimate optimal policy. We evaluate our approach in a simulation platform and report some initial results concerning its performance on estimating optimal navigation policies to unknown environments under different environmental disturbances. The results are promising.