Pham, B.T. and Prakash, I. and Singh, S.K. and Shirzadi, A. and Shahabi, H. and Tran, T.-T.-T. and Bui, D.T. (2019) Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches. Catena, 175. pp. 203-218. ISSN 3418162
Full text not available from this repository. (Upload)Abstract
Nowadays, a number of machine learning prediction methods are being applied in the field of landslide susceptibility modeling of the large area especially in the difficult hilly terrain. In the present study, hybrid machine learning approaches of Reduced Error Pruning Trees (REPT) and different ensemble techniques were used for the construction of four novel hybrid models namely Bagging based Reduced Error Pruning Trees (BREPT), MultiBoost based Reduced Error Pruning Trees (MBREPT), Rotation Forest-based Reduced Error Pruning Trees (RFREPT), Random Subspace-based Reduced Error Pruning Trees (RSREPT) for landslide susceptibility assessment and prediction. In total, ten topographical and geo-environmental landslide conditioning factors were considered for analyzing their spatial relationship with landslide occurrences. Receiver Operating Characteristic curve, Statistical Indexes, and Root Mean Square Error methods were used to validate performance of these models. Analysis of model results indicate that the BREPT is the best model for landslide susceptibility assessment in comparison to other models. © 2018 Elsevier B.V.
Item Type: | Article |
---|---|
Divisions: | Institutes > Institute of Techniques for Special Engineering |
Identification Number: | 10.1016/j.catena.2018.12.018 |
Uncontrolled Keywords: | conditioning; error analysis; hillslope; landslide; machine learning; numerical method; operations technology; prediction |
Additional Information: | Language of original document: English. |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/9368 |