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.

中文翻译：

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基于超管道算法的新型整体滑坡预测模型：以越南南坝公社为例

具有强大预测能力的滑坡预测模型的开发已成为许多研究人员的主要研究重点。这项研究描述了Hyperpipes（HP）算法在五个新的集成模型开发中的首次应用，这些模型将HP算法与AdaBoost（AB），Bagging（B），Dagging，Decorate和Real AdaBoost（RAB）集成在一起越南河江省南丹公社的滑坡敏感性空间变异图绘制技术。有关76个历史滑坡和十个地质环境因素（坡度，坡向，高程，地形湿度指数，曲率，风化壳，地质，河流密度，断层密度，和距道路的距离）用于构建训练和验证数据集，这是构建和测试所提出模型的先决条件。使用不同的性能指标（即接收器工作特征曲线（AUC）下的面积，负预测值，正预测值，准确性，灵敏度，特异性，均方根误差和Kappa），我们验证了这五个集合的熟练程度学习技术，以提高基本HP模型的适应性和预测能力。根据从模型得出的AUC值，可以将产生AUC值为0.922的整体ABHP模型确定为最有效的制图南丹公社滑坡敏感性的模型，其次是RABHP（AUC = 0.919），BHP（ AUC = 0.909），Dagping-HP（AUC = 0.897），Decorate-HP（AUC = 0。865）和单个HP模型（AUC = 0.856）。为南丹公社提出的新型合奏模型和由此产生的敏感性图可以帮助土地利用规划人员制定有效的减灾策略，以应对破坏性滑坡。