The mechanism of landslide occurrence is complicated due to the dependency and nonlinear relationship of various physical factors. A promising prediction model, which can be used to locate the high-risk regions and a corresponding prevention strategy can be prepared to reduce the slide occurrence and its consequence, is therefore desired. To perform the landslide assessment for a large-scale slope, this study proposes to use the method of small watershed that is integrated with Relevance Vector Machine (RVM) to enhance the prediction accuracy. Effect of physiographic and hydrological factors such as slope steepness, dip slope ratio, landslide ratio, and cumulative rainfall are investigated. To estimate the occurrence of landslide, RVM first maps the aforementioned factors into a feature space using Gaussian radial basis function. A linear boundary, distinguishing landslide or not, is then obtained through the search of the optimal weights. To find these weights, a Bayesian theory-based optimization problem is formulated and solved by iteratively reweighted least squares algorithm and the Laplace approximation procedure. The proposed model is validated by the data collected from Kaoping River Basin. Results indicate that the proposed RVM-based small watershed approach possesses a prediction accuracy of 87.5%, which is better than those of using Support Vector Machine (SVM), Least-Square Support Vector Machine (LS-SVM), and logistic regression, providing authorities in their hazard alert system to minimize the life or property losses caused by the landslide.

中文翻译：

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相关向量机的小流域估计滑坡发生。

由于各种物理因素的依赖性和非线性关系，滑坡发生的机理很复杂。因此，需要一种有前途的预测模型，该模型可用于定位高风险区域，并可以制定相应的预防策略以减少滑坡的发生及其后果。为了对大尺度斜坡进行滑坡评估，本研究建议使用与相关向量机（RVM）集成的小流域方法来提高预测精度。研究了诸如坡度，坡度比，滑坡比和累积降雨等生理和水文因素的影响。为了估计滑坡的发生，RVM首先使用高斯径向基函数将上述因素映射到特征空间中。线性边界 然后，通过搜索最佳权重来获得是否区分滑坡。为了找到这些权重，通过迭代加权最小二乘算法和拉普拉斯逼近程序，提出并解决了基于贝叶斯理论的优化问题。所提出的模型已通过从高平河流域收集的数据进行了验证。结果表明，所提出的基于RVM的小流域方法具有87.5％的预测准确度，优于使用支持向量机（SVM），最小二乘支持向量机（LS-SVM）和逻辑回归的预测精度，当局在其灾害预警系统中将滑坡造成的生命或财产损失降至最低。通过迭代加权最小二乘算法和拉普拉斯逼近程序，提出并解决了基于贝叶斯理论的优化问题。所提出的模型已通过从高平河流域收集的数据进行了验证。结果表明，所提出的基于RVM的小流域方法具有87.5％的预测准确度，优于使用支持向量机（SVM），最小二乘支持向量机（LS-SVM）和逻辑回归的预测精度，当局在其灾害预警系统中将滑坡造成的生命或财产损失降至最低。通过迭代加权最小二乘算法和拉普拉斯逼近程序，提出并解决了基于贝叶斯理论的优化问题。所提出的模型已通过从高平河流域收集的数据进行了验证。结果表明，所提出的基于RVM的小流域方法具有87.5％的预测准确度，优于使用支持向量机（SVM），最小二乘支持向量机（LS-SVM）和逻辑回归的预测精度，当局在其灾害预警系统中将滑坡造成的生命或财产损失降至最低。