A Comparative Study of Landslide Susceptibility Evaluation Based on Three Different Machine Learning Algorithms
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摘要:
准确的滑坡易发性评价结果是山区滑坡灾害防治的关键，可有效规避潜在滑坡带来的风险。为获得准确、可靠的滑坡预防参考，笔者以云南芒市为研究对象，选取高程、地层岩性、年均降雨量等9项评价因子，通过多重共线性分析，构建研究区滑坡易发性评价指标体系。分别基于支持向量机（SVM）、BP神经网络和随机森林（RF）3种典型机器学习算法进行滑坡易发性评价。利用准确性（ACC）、ROC曲线下面积（AUC）、滑坡比（Sei）及野外实地考察对模型评价结果精度进行对比验证分析。结果显示RF模型的ACC、AUC和极高易发区的SeV值最高，分别为0.867、0.94、9.21；BP神经网络模型次之，其SeV值分别为0.829、0.90、9.14；SVM最低，其SeV值分别为0.794、0.88、6.85。此外，RF算法所得结果还与实地考察情况保持了较高的一致性。实验结果表明与其他两种算法相比，RF算法在芒市区域具有更高的准确性和可靠性，更适合用于该区域的滑坡易发性建模，且利用该模型获得的评价结果，能够为芒市区域的滑坡防治提供理论依据和科学参考。
Abstract:Accurate landslide susceptibility evaluation results are the key to landslide disaster prevention and control in mountainous areas, which can effectively avoid the risk caused by potential landslides. To obtain an accurate and reliable reference for landslide prevention, this paper selects nine evaluation factors, including elevation, stratigraphic lithology, average annual rainfall et al, and constructs a landslide susceptibility evaluation index system in the study area through multiple covariance analysis, taking Mangcheng, Yunnan Province as the research object. Subsequently, three typical machine learning models based on support vector machine (SVM), BP neural network and random forest (RF) were used for landslide susceptibility evaluation. Finally, the accuracy of the model evaluation results was compared and validated by using accuracy (ACC), area under the ROC curve (AUC), landslide ratio (Sei) and field fieldwork. The results showed that the RF model had the highest SeV values of 0.867, 0.94 and 9.21 for ACC, AUC, and very high susceptibility areas, respectively; the BP neural network model had the second highest values of 0.829, 0.90 and 9.14; the SVM had the lowest values of 0.794, 0.88 and 6.85; and the RF model results were more consistent with the field study. The results of experiments show that compared with the other two algorithms, the RF algorithm has higher accuracy and reliability in the Mangshi region and is more suitable for landslide susceptibility modeling in the region, and the evaluation results obtained by using the model can provide a theoretical basis and scientific reference for landslide control in the Mangshi region.
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Key words:
- SVM /
- BP neural networks /
- RF /
- landslide susceptibilit /
- Mangshi city
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表 1 评价因子多重共线性分析结果
Table 1. Results of multiple covariance analysis of evaluation factors
评价因子 容差 VIF 评价因子 容差 VIF 高程 0.781 1.281 起伏度 0.176 5.693 坡度 0.159 6.298 地层岩性 0.990 1.010 坡向 0.979 1.022 年均降雨量 0.981 1.019 平面曲率 0.708 1.413 土地利用 0.984 1.017 剖面曲率 0.869 1.151 
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表 2 测试样本精度评价
Table 2. Test sample accuracy evaluation
评价指标 评价模型 SVM BP神经网络 RF TP（真阳性） 130 143 138 FP（假阳性） 29 31 29 TN（真阴性） 139 138 156 FN（假阴性） 41 27 16 ACC（准确度） 0.794 0.829 0.867
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表 3 易发性分区合理性检验结果
Table 3. Rationality test results of susceptibility zoning
评价
模型易发区 分级面
积/km2滑坡
点/个Mei Dei Sei SVM 极低（I） 1260.04 47 43.51% 8.32% 0.19 低（II） 624.7 47 21.57% 8.32% 0.39 中（III） 449.2 49 15.51% 8.67% 0.56 高（IV） 328.04 109 11.33% 19.29% 1.70 极高（V） 234.11 313 8.08% 55.40% 6.85 BP神经
网络极低（I） 1282.66 27 44.29% 4.78% 0.11 低（II） 619.45 31 21.39% 5.49% 0.26 中（III） 443.81 42 15.32% 7.43% 0.49 高（IV） 330.44 73 11.41% 12.92% 1.13 极高（V） 219.73 392 7.59% 69.38% 9.14 RF 极低（I） 1262.13 17 43.58% 3.00% 0.07 低（II） 684.86 20 23.65% 3.54% 0.15 中（III） 422.73 37 14.60% 6.55% 0.45 高（IV） 287.12 61 9.91% 10.80% 1.09 极高（V） 239.25 430 8.26% 76.11% 9.21
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