Abstract
We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but “distance to road” was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas.
Highlights
Landsliding is a complex natural phenomenon and is responsible for considerable loss of life and damage to engineered infrastructure worldwide [1]
This result indicates that all 20 hadthe significance in the landslide susceptibility most of observed landslides the Sarkhoon are located near roads
We developed the models using 98 landslide locations and 20 conditioning factors tested by the One-R Attribute Evaluation (ORAE) technique
Summary
Landsliding is a complex natural phenomenon and is responsible for considerable loss of life and damage to engineered infrastructure worldwide [1]. Considerable efforts are being made to understand the causes and triggers of landslides to reduce these losses [2]. Considerable research has been carried out to identify and classify terrain that is susceptible to landslides. A common key strategy is to prepare a landslide susceptibility map that shows areas that are prone to landslides within a region of interest. Such maps can provide understandable and useful information that helps planners better manage susceptible areas [3,4,5]. There is no universal approach for preparing landslide susceptibility maps, landslide researchers are experimenting with new methods and techniques [6]. Novel quantitative approaches proposed by, researchers in recent years include logistic regression [7,8,9], multivariate regression [10,11,12], discriminant analysis [13,14], certainty factor [15,16], index or entropy (IOE) [17,18], spatial multi-criteria evaluation (SMCE) [19], statistical index [20,21], and analytic hierarchy processes [22,23]
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