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• W3044360060 startingPage "104805" @default.
• W3044360060 abstract "Using multiple ensemble learning techniques for improving the predictive accuracy of landslide models is an active research area. In this study, we combined a radial basis function (RBF) neural network (RBFN) with the Random Subspace (RSS), Attribute Selected Classifier (ASC), Cascade Generalization (CG), Dagging for spatial prediction of landslide susceptibility in the Van Chan district, Yen Yen Bai Province, Vietnam. A geospatial database that contained records from 167 historical landslides and 12 conditioning factors (slope, aspect, elevation, curvature, slope length, valley depth, topographic wetness index, and terrain ruggedness index, and distance to rivers, roads, and faults) were used to develop the ensemble models. The models were validated via area under the receiver operating characteristic curve (AUC) and several other performance metrics (i.e., positive predictive value, negative predictive value, sensitivity, specificity, accuracy, and Kappa). Although the single RBFN model (AUC = 0.799) performed better than the ensemble models (AUCaverage = 0.77) in the training phase, the ensemble models (AUCaverage = 0.83) outperformed RBFN (AUC = 0.79) in the validation phase, demonstrating superior predictive performance of the ensemble models for the prediction of future landslides. Our study provides insights for developing reliable landslide predictive models for different landslide-prone regions around the world." @default.
• W3044360060 created "2020-07-29" @default.
• W3044360060 creator A5027233897 @default.
• W3044360060 creator A5029814958 @default.
• W3044360060 creator A5037364559 @default.
• W3044360060 creator A5051780339 @default.
• W3044360060 creator A5053998535 @default.
• W3044360060 creator A5056909844 @default.
• W3044360060 creator A5073893774 @default.
• W3044360060 creator A5081665504 @default.
• W3044360060 date "2020-12-01" @default.
• W3044360060 modified "2023-10-16" @default.
• W3044360060 title "Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping" @default.
• W3044360060 cites W1955018252 @default.
• W3044360060 cites W1964965599 @default.
• W3044360060 cites W1970227794 @default.
• W3044360060 cites W2006968845 @default.
• W3044360060 cites W2057388082 @default.
• W3044360060 cites W2058082754 @default.
• W3044360060 cites W2073678701 @default.
• W3044360060 cites W2093390311 @default.
• W3044360060 cites W2125818553 @default.
• W3044360060 cites W2128954654 @default.
• W3044360060 cites W2133990480 @default.
• W3044360060 cites W2143593953 @default.
• W3044360060 cites W2243686343 @default.
• W3044360060 cites W228909357 @default.
• W3044360060 cites W2338364858 @default.
• W3044360060 cites W2511553367 @default.
• W3044360060 cites W2579296223 @default.
• W3044360060 cites W2600016337 @default.
• W3044360060 cites W2601486684 @default.
• W3044360060 cites W2619690791 @default.
• W3044360060 cites W2623562594 @default.
• W3044360060 cites W2707229801 @default.
• W3044360060 cites W2736739107 @default.
• W3044360060 cites W2758350461 @default.
• W3044360060 cites W2765107801 @default.
• W3044360060 cites W2767689310 @default.
• W3044360060 cites W2775745878 @default.
• W3044360060 cites W2778978672 @default.
• W3044360060 cites W2783350994 @default.
• W3044360060 cites W2785033958 @default.
• W3044360060 cites W2787163266 @default.
• W3044360060 cites W2789555074 @default.
• W3044360060 cites W2791165267 @default.
• W3044360060 cites W2792546905 @default.
• W3044360060 cites W2799444970 @default.
• W3044360060 cites W2802780461 @default.
• W3044360060 cites W2809363322 @default.
• W3044360060 cites W28412257 @default.
• W3044360060 cites W2842776424 @default.
• W3044360060 cites W2883429111 @default.
• W3044360060 cites W2884613110 @default.
• W3044360060 cites W2890029039 @default.
• W3044360060 cites W2898385887 @default.
• W3044360060 cites W2905155550 @default.
• W3044360060 cites W2907066318 @default.
• W3044360060 cites W2907556463 @default.
• W3044360060 cites W2909188960 @default.
• W3044360060 cites W2911253733 @default.
• W3044360060 cites W2911673646 @default.
• W3044360060 cites W2912573428 @default.
• W3044360060 cites W2912796358 @default.
• W3044360060 cites W2913214568 @default.
• W3044360060 cites W2919749284 @default.
• W3044360060 cites W2922019787 @default.
• W3044360060 cites W2922202880 @default.
• W3044360060 cites W2923370583 @default.
• W3044360060 cites W2938393691 @default.
• W3044360060 cites W2943973631 @default.
• W3044360060 cites W2946047567 @default.
• W3044360060 cites W2946648277 @default.
• W3044360060 cites W2951213714 @default.
• W3044360060 cites W2962207954 @default.
• W3044360060 cites W2965758891 @default.
• W3044360060 cites W2968023343 @default.
• W3044360060 cites W2971699730 @default.
• W3044360060 cites W2972075568 @default.
• W3044360060 cites W2972082796 @default.
• W3044360060 cites W2975358916 @default.
• W3044360060 cites W2985766090 @default.
• W3044360060 cites W2988636483 @default.
• W3044360060 cites W2989676862 @default.
• W3044360060 cites W2993717253 @default.
• W3044360060 cites W2995027684 @default.
• W3044360060 cites W2995502771 @default.
• W3044360060 cites W2996089053 @default.
• W3044360060 cites W2997037219 @default.
• W3044360060 cites W2998470349 @default.
• W3044360060 cites W2999015335 @default.
• W3044360060 cites W2999226650 @default.
• W3044360060 cites W2999310044 @default.
• W3044360060 cites W3006583570 @default.
• W3044360060 cites W3008924545 @default.
• W3044360060 cites W3010479350 @default.
• W3044360060 cites W3011171922 @default.
• W3044360060 cites W3011540643 @default.

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