Kazuhide Sawada

TL;DR: In this article, a small-scale laboratory physical modeling of sand flow at different slopes and measuring the impact force exerted by this material on a fixed rigid wall was conducted to estimate the quasi-static force generated by the sediment as it comes to rest on the wall following impact.

TL;DR: In this article, a landslide inventory was partitioned into three groups as various training and test datasets to identify the most appropriate method for creating a landslide susceptibility map, and a total of fifteen landslide susceptibility maps were produced using frequency ratio, logistic regression, decision tree, weights of evidence and artificial neural network models, and the results were assessed using existing test landside points and areas under the relative operative characteristic curve.

TL;DR: In this article, three mathematical methods, logistic regression (LR), bivariate statistical analysis (BS), and multivariate adaptive regression spline models (MARSplines), were used to create landslide susceptibility maps by comparing the past landslide distribution and the conditioning factor thematic maps.

TL;DR: The Fuzzy c-means (FCM) algorithm is presented as an optimal method for choosing the appropriate input landslide points as training data and revealed that Model no. 4 and model no. 5 offer better model results for landslide susceptibility mapping.

TL;DR: Wang et al. as discussed by the authors proposed a smoothed particle hydrodynamics (SPH) modeling technique to simulate the post-earthquake debris flows in the Wenchuan earthquake disaster areas.