Universiti Teknologi Malaysia Institutional Repository

New ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed

Bui, Dieu Tien and Shirzadi, Ataollah and Shahabi, Himan and Geertsema, Marten and Omidvar, Ebrahim and Clague, John J. and Pham, Binh Thai and Dou, Jie and Asl, Dawood Talebpour and Ahmad, Baharin and Lee, Saro (2019) New ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed. Forests, 10 (9). p. 743. ISSN 1999-4907

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Official URL: http://dx.doi.org/10.3390/f10090743

Abstract

We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-wbakhtiari, 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.

Item Type:Article
Uncontrolled Keywords:Factor selection, GIS
Subjects:N Fine Arts > NA Architecture
Divisions:Built Environment
ID Code:87625
Deposited By: Widya Wahid
Deposited On:30 Nov 2020 09:06
Last Modified:30 Nov 2020 09:06

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