Prediction of Mountain Road Closure Due to Rainfall-Induced Landslides

Abstract

The implementation of landslide probability analysis was undertaken to evaluate the effect of landsliding on closures of major mountain road networks at Guoshin Township in central Taiwan. To achieve this objective, an event-based landslide probability analysis method was adopted to establish a landslide prediction model by using a set of training data from the landslides triggered by Typhoon Mindulle in July 2004. Landslide causative factors and triggering factors were selected in a logistic regression scheme so that the criteria for successfully distinguishing landslides from nonlandslides were established. Landslide occurrence probability was mapped in the whole study area and along the road route. Locations of high potential for landslide occurrence were, thus, highlighted along the road route and were proposed for road closure during typhoon events. At last, the proposed locations for road closure were validated by historical road closures caused by subsequent typhoons after Typhoon Mindulle. Validation results show that the proposed model could be used in predicting road closure resulting from storm-induced landslides.

Bayesian Classifier with K-Nearest Neighbor Density Estimation for Slope Collapse Prediction

“…1, the structure of Bayesian classification relies upon the prior probabilities P(C k ) and the conditional densities P(X|C k ) (Theodoridis & Koutroumbas 2009). The first quantity can be estimated directly from the distribution of the training samples among classes (Clark & Niblett 1989).…”

“…This section of the paper reviews the K-NN algorithm for density estimation (Theodoridis & Koutroumbas 2009). Consider a set of N data points, X 1 , X 2 , …, X N ∈ R D generated from an unknown statistical distribution, the goal is to estimate the value of the unknown density function at a given point X o .…”

Geologic Hazard Assessment of Slopeland Villages Using Remote Sensing Techniques

“…In the past, relevant domestic and international studies have used geologic hazard investigation and environmental geology data sets to construct a data set and utilized remote sensing and image interpretation and field investigation methods to provide a foundation for landslide susceptibility assessments [1][2][3]. The benefit of utilizing remote sensing and image interpretation is the relatively short time needed to perform large scale geologic hazard assessments and ability to overcome difficulties associated with rough and dangerous terrain in the field [4]. Resultantly, this study utilizes remote sensing and image interpretation techniques, obtains data immeasurable from the remote sensing images.…”