Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam

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المعرّف: 113044
2020
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الملخص
Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides.
المفتاح المرجعي
pham2020appliednovel استخدم هذا المفتاح للاستشهاد التلقائي في المخطوطة أثناء استخدام مدير المخطوطات أو مدير الأطروحات من SciMatic
المؤلفون Quoc Cuong Tran,Do Minh Duc,Abolfazl Jaafari,Nadhir Al-Ansari,Duc Dao Minh,Duc Tung Van,Duc Anh Nguyen,Trung Hieu Tran,Lanh Si Ho,Huu Duy Nguyen,Indra Prakash,Hiep Van Le,Binh Thai Pham;Quoc Cuong Tran;Do Minh Duc;Abolfazl Jaafari;Nadhir Al-Ansari;Duc Dao Minh;Duc Tung Van;Duc Anh Nguyen;Trung Hieu Tran;Lanh Si Ho;Huu Duy Nguyen;Indra Prakash;Hiep Van Le;Binh Thai Pham;
مجلة applied sciences
السنة 2020
DOI
10.3390/app10113710
URL
https://sciprofiles.com/publication/view/0b4a23ca6eb71eea706cec84a2f4c42f
الكلمات المفتاحية
Machine learning bagging adaboost ensemble modeling dagging real adaboost decorate Machine learning bagging adaboost ensemble modeling dagging real adaboost decorate

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