This paper proposes a novel method to incorporate unfavorable orientations of discontinuities into machine learning (ML) landslide prediction by using GIS-based kinematic analysis. Discontinuities, detected from photogrammetric and aerial LiDAR surveys, were included in the assessment of potential rock slope instability through GIS-based kinematic analysis. Results from the kinematic analysis, coupled with several commonly used landslide influencing factors, were adopted as input variables in ML models to predict landslides. In this paper, various ML models, such as random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and deep learning neural network (DLNN) models were evaluated. Results of two validation methods (confusion matrix and ROC curve) show that the involvement of discontinuity-related variables significantly improved the landslide predictive capability of these four models. Their addition demonstrated a minimum of 6% and 4% increase in the overall prediction accuracy and the area under curve (AUC), respectively. In addition, frequency ratio (FR) analysis showed good consistency between landslide probability that was characterized by FR values and discontinuity-related variables, indicating a high correlation. Both results of model validation and FR analysis highlight that inclusion of discontinuities into ML models can improve landslide prediction accuracy.

中文翻译：

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最大化遥感测量对边坡稳定性的影响-一种将不连续性纳入机器学习滑坡预测的新方法

本文提出了一种新的方法，该方法通过使用基于GIS的运动学分析方法将不连续的不利方向纳入机器学习（ML）滑坡预测中。通过基于GIS的运动学分析，从摄影测量和航空LiDAR测量中发现的不连续性被包括在潜在岩质边坡不稳定性评估中。运动学分析的结果，加上几种常用的滑坡影响因素，被用作机器学习模型中的输入变量，以预测滑坡。本文评估了各种ML模型，例如随机森林（RF），支持向量机（SVM），多层感知器（MLP）和深度学习神经网络（DLNN）模型。两种验证方法（混淆矩阵和ROC曲线）的结果表明，与间断性相关的变量的参与显着提高了这四个模型的滑坡预测能力。他们的加入分别表明整体预测精度和曲线下面积（AUC）分别至少增加了6％和4％。此外，频率比（FR）分析显示，以FR值为特征的滑坡概率与不连续性相关变量之间具有良好的一致性，表明相关性很高。模型验证和FR分析的结果都表明，将不连续性纳入ML模型可以提高滑坡预测的准确性。他们的加入分别表明整体预测精度和曲线下面积（AUC）分别至少增加了6％和4％。此外，频率比（FR）分析显示，以FR值为特征的滑坡概率与不连续性相关变量之间具有良好的一致性，表明相关性很高。模型验证和FR分析的结果都表明，将不连续性纳入ML模型可以提高滑坡预测的准确性。他们的加入分别表明整体预测精度和曲线下面积（AUC）分别至少增加了6％和4％。此外，频率比（FR）分析显示，以FR值为特征的滑坡概率与不连续性相关变量之间具有良好的一致性，表明相关性很高。模型验证和FR分析的结果都表明，将不连续性纳入ML模型可以提高滑坡预测的准确性。