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首页> 外文期刊>Annals of the American Thoracic Society >Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China
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Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China

机译：中国浙江省丽水市山坡山坡机械学习方法的预测能力

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The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.
机译：本研究的主要目标是使用合成少数群体过采样技术（SMOTE）来扩展机器学习方法的滑坡样品数量（即支持向量机（SVM），逻辑回归（LR），人工神经网络（ANN）随机森林（RF））为中国浙江省丽水市生产高质量的滑坡易感性图。从地形图，地质图和卫星图像中提取了与滑坡相关的因素。使用相关系数分析和邻域粗糙集（NRS）方法作为独立变量选择12个因素。总共使用现场调查，历史记录和卫星图像映射了288个土地滑坡。山体滑坡被随机分为两个数据集：70％的所有山体滑坡被选为原始训练数据集，30％用于验证。然后，使用SMOTE来生成带有尺寸的数据集，其训练数据集的训练数据集的两个到三十次来建立和比较山体滑坡易感映射的四种机器学习方法。此外，我们使用坡度单位来细分地形以确定滑坡易感性。最后，使用统计指标和曲线下的区域（AUC）验证滑坡易感性图。结果表明，随着样品尺寸的增加，四种机器学习方法的性能显示出不同的改善程度。 RF模型表现出比ANN（18.94％），SVM（17.77％）和LR（3.00％）模型更具实质性的改进（AUC提高24.12％）。此外，ANN模型实现了最高的预测能力（AUC = 0.98），然后是RF（AUC = 0.96），SVM（AUC = 0.94）和LR（AUC = 0.79）模型。这种方法显着提高了机器学习技术对滑坡易感性映射的性能，从而提供了更好的工具来减少滑坡灾害的影响。

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