New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed

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ID: 260800
2019
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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.
Reference Key
bui2019forestsnew Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dieu Tien Bui;Ataollah Shirzadi;Himan Shahabi;Marten Geertsema;Ebrahim Omidvar;John J. Clague;Binh Thai Pham;Jie Dou;Dawood Talebpour Asl;Baharin Bin Ahmad;Saro Lee;Tien Bui, Dieu;Shirzadi, Ataollah;Shahabi, Himan;Geertsema, Marten;Omidvar, Ebrahim;Clague, John J.;Thai Pham, Binh;Dou, Jie;Talebpour Asl, Dawood;Bin Ahmad, Baharin;Lee, Saro;
Journal forests
Year 2019
DOI
10.3390/f10090743
URL
https://dx.doi.org/10.3390/f10090743
Keywords
Machine learning Iran gis goodness-of-fit shallow landslide factor selection

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