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

By D. Tien Bui, Ataollah Shirzadi, Himan Shahabi, Marten Geertsema, Ebrahim Omidvar, John J. Clague, Binh Thai Pham, Jie Dou, Dawood Talebpour Asl, Baharin bin Ahmad and 1 more. Forests, 2019

114 citations recorded by the source corpus.

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.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/88b0234536356619e6825beac3bbce390d4e1c85

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. Ataollah Shirzadi
2. Himan Shahabi
3. Kamran Chapi
4. N. Al‐Ansari
5. Jie Dou

In Management, Monitoring, Policy and Law

1. Ataollah Shirzadi
2. Kamran Chapi
3. Jie Dou
4. Himan Shahabi

Citations in the corpus

• Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran (2020, background): “A wide variety of machine learning models are now in use in natural hazard research, including: artificial neural network (ANN) [39,40], adaptive neuro-fuzzy inference system (ANFIS) [41–44], support vector machine (SVM) [15,45,46], K-nearest neighbor (KNN) [47], logistic model tree (LMT) [48,49], a”