| 考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律 |
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| 引用本文： | 黄发明,李金凤,王俊宇,毛达雄,盛明强.考虑线状环境因子适宜性和不同机器学习模型的滑坡易发性预测建模规律[J].地质科技通报,2022,41(2):44-59. |
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| 作者姓名： | 黄发明 李金凤 王俊宇 毛达雄 盛明强 |
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| 基金项目： | 国家自然科学基金项目41807285国家自然科学基金项目52069013国家重点研发计划项目2019YFC0605001中国博士后基金项目2019M652287中国博士后基金项目2020T130274江西省博士后基金项目2019KY08地灾防治与环境保护国家重点实验室开放基金项目SKLGP2021K012 |
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| 摘 要： | 对于滑坡易发性预测中的水系、公路和断层等线状环境因子, 现有研究大多采用缓冲分析提取距离线状因子的距离。但缓冲分析得到的线距离属于离散型变量, 带有大小不等的随机波动性且对点或线要素的误差较为敏感, 导致滑坡易发性建模精度下降。提出了使用水系和公路的空间密度等连续型变量改进线状环境因子的适宜性。以江西省安远县为例, 选取高程、地形起伏度、距水系和公路距离等14个环境因子(原始因子), 再将距水系和公路距离2个线状因子改进为水系密度和公路密度(改进因子); 之后采用逻辑回归、多层感知器、支持向量机和C5.0决策树等机器学习模型, 分别构建了基于原始因子和改进因子的机器学习模型以预测滑坡易发性; 最后利用ROC曲线和易发性指数分布特征等来研究建模规律。结果表明: ①改进因子机器学习预测精度均高于原始因子机器学习模型, 表明空间密度对于易发性预测的适宜性更好; ②在4类机器学习模型中C5.0模型对于滑坡易发性预测性能最好, 其次是SVM、MLP和LR; ③水系和公路两类环境因子的重要性较高且使用改进因子机器学习后这两类环境因子重要性排名依然非常靠前。
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| 关 键 词： | 滑坡易发性预测 线状环境因子 空间密度 机器学习 地理信息系统 |
| 收稿时间： | 2021-07-15 |
Modelling rules of landslide susceptibility prediction considering the suitability of linear environmental factors and different machine learning models |
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| Abstract: | For linear environmental factors such as river, road and geological fault networks, buffer analysis in GIS is commonly used to extract the buffer distances to the river and/or road networks. However, the line distances are discrete variables with random fluctuations of different grid sizes and are more sensitive to the errors of point and/or line elements, leading to a reduction in the accuracy of landslide susceptibility prediction (LSP). This study aims to use continuous environmental factors, such as the spatial density of river and road networks, to improve the suitability of linear environmental factors. Taking An'yuan County of Jiangxi Province as an example, 14 environmental factors, such as elevation, topographic relief, distances to river and road networks (original factors), are selected. Then, the two linear environmental factors of distances to river and road networks are improved to river and road density (improved factors). Based on machine learning models such as logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM) and C5.0 decision tree, original factor-based and improved factor-based machine learning models are built to carry out the LSP. The receiver operating characteristic (ROC) curves and the distribution characteristics of landslide susceptibility indexes are used to evaluate the LSP modelling rules. The results show that ① the LSP accuracy of the improved factor-based models are higher than those of the original factor-based models, indicating that the spatial density is more suitable for LSP; ② the C5.0 model has the best LSP performance among the four machine learning models, followed by the SVM, MLP and LR models; and ③ river and road factors are of great significance for landslide evolution, and their importance does not decrease underimproved factor-based machine learning models. |
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