Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

Abstract

Forward logistic regression has allowed us to derive an earth-flow susceptibility model for the Tumarrano river basin, which was defined by modeling the statistical relationships between an archive of 760 events and a set of 20 predictors. For each landslide in the inventory, a landslide identification point (LIP) was automatically produced as corresponding to the highest point along the boundary of the landslide polygons, and unstable conditions were assigned to cells at a distance up to 8m. An equal number of stable cells (out of landslides) was then randomly extracted and appended to the LIPs to prepare the dataset for logistic regression. A model building strategy was applied to enlarge the area included in training the model and to verify the sensitivity of the regressed models with respect to the locations of the selected stable cells. A suite of 16 models was prepared by randomly extracting different unoverlapping stable cell subsets that have been appended to the unstable ones. Models were finally submitted to forward logistic regression and validated. The results showed satisfying and stable error rates (0.236 on average, with a standard deviation of 0.007) and areas under the receiver operating characteristic (ROC) curve (AUCs) (0.839 for training and 0.817 for test datasets) as well as factor selections (ranks and coefficients). As regards the predictors, steepness and large-profile and local-plan topographic curvatures were systematically selected. Clayey outcropping lithology, midslope drainage, local and midslope ridges, and canyon landforms were also very frequently (from eight to 15 times) included in the models by the forward selection procedures. The model-building strategy allowed us to produce a performing earth-flow susceptibility model, whose model fitting, prediction skill, and robustness were estimated on the basis of validation procedures, demonstrating the independence of the regressed model on the specific selection of the stable cells.

Landslide susceptibility mapping on a global scale using the method of logistic regression

“…The quantity of nonlandslide points should be carefully considered. Most studies use an equal number of landslide points and nonlandslide points (Dai and Lee, 2002;Kawabata and Bandibas, 2009;Chau and Chan, 2005;Costanzo et al, 2014;Regmi et al, 2014;Mathew et al, 2009). However, a few authors prefer an unequal number Felicisimo et al, 2013).…”

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

“…However, it is not possible in many areas due to limitation of land and the rapid growth of human population [95], therefore high quality of landslide susceptibility and hazards maps is an important tool for reducing landslide risk through landuse planning and management. However, the prediction performances of landslide susceptibility models is still one of the most debated subjects in recent decades [96]. Literature review shows that a perfect landslide model that makes no error is almost impossible; therefore, a highly accurate model for particular area requires assessment studies to find an algorithm with the highest overall performances.…”

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides Using GIS

“…However, it is not possible in many areas due to limitation of land and the rapid growth of human population [89], therefore high quality of landslide susceptibility and hazards maps is an important tool for reducing landslide risk through landuse planning and management. However, the prediction performances of landslide susceptibility models is still one of the most debated subjects in recent decades [90]. Literature review shows that a perfect landslide model which makes no error is almost impossible; therefore a highly accurate model for particular area requires assessment studies to find an algorithm with the highest overall performances.…”