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首页> 外文期刊>Bulletin of engineering geology and the environment >A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling
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A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

机译：一种新型混合智能模型的支持向量机与山体滑坡敏感性建模的多功能集合

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摘要

The main aim of this study is to propose a novel hybrid intelligent model named MBSVM which is an integration of the MultiBoost ensemble and a support vector machine (SVM) for modeling of susceptibility of landslides in the Uttarakhand State, Northern India. Firstly, a geospatial database for the study area was prepared, which includes 391 historical landslides and 16 landslide-affecting factors. Then, the sensitivity of different combinations of these factors for modeling was validated using the forward elimination technique. The MBSVM landslide model was built using the datasets generated from the best selected factors and validated utilizing the area under the receiver operating characteristic (ROC) curve (AUC), statistical indexes, and the Wilcoxon signed-rank test. Results show that this novel hybrid model has good performance both in terms of goodness of fit with the training dataset (AUC=0.972) and the capability to predict landslides with the testing dataset (AUC=0.966). The efficiency of the proposed model was then validated by comparison with logistic regression (LR), a single SVM, and another hybrid model of the AdaBoost ensemble and an SVM (ABSVM). Comparison results show that the MBSVM outperforms the LR, single SVM, and hybrid ABSVM models. Thus, the proposed model is a promising and good alternative tool for landslide hazard assessment in landslide-prone areas.
机译：本研究的主要目的是提出一种名为MBSVM的新型混合智能模型，该模型是Multiboost集合和支持向量机（SVM）的集成，用于印度北部北方北部堤岸稳定性的易感性易感性。首先，制备了研究区域的地理空间数据库，其中包括391个历史山体滑坡和16个山体滑坡影响因素。然后，使用前向消除技术验证了这些因素的不同组合的敏感性。 MBSVM Landslide模型是使用从最佳选定因子产生的数据集建立，并在接收器操作特征（ROC）曲线（AUC）下的区域，统计指标和Wilcoxon签名 - 等级测试中验证。结果表明，这种新颖的混合模型在拟合训练数据集（AUC = 0.972）方面具有良好的性能，以及预测与测试数据集（AUC = 0.966）的滑坡的能力。然后通过与逻辑回归（LR），单个SVM和ADABOOST集合的另一混合模型和SVM（ABSVM）进行验证，通过比较验证所提出的模型的效率。比较结果表明，MBSVM优于LR，单个SVM和混合动力ABSVM模型。因此，拟议的模型是Landslide-Prone地区滑坡危害评估的有前途和良好的替代工具。

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