Abstract

Landslide analysis prevents landslides from threatening resident safety and property, and the predominant method is susceptibility assessment which is cumbersome and time-consuming. The association rule algorithm (ARA) is proposed to mine the correlation between the factors and landslides simply and rapidly. The original ARA cannot reflect the scope of landslides which is non-negligible for landslide analysis and is thus improved to mine the frequent secondary-factor combinations (SFCs). Firstly, eight factors are selected using the out-of-bag error and chi-squared (${\mathrm{\chi }}^2$) test. The accuracy of the factor selection is further verified employing landslide susceptibility assessment which is predicted using 30% of study grid data selected randomly as the training data. The improved ARA employs the area of historical landslides to mine the frequent SFCs, and the results are then verified by the frequency ratio and ${\mathrm{\chi }}^2$ test. It is concluded that the frequent SFCs are: (21, 41), (21, 74), (34, 41), (34, 74), (41, 74), (21, 41, 74), and (34, 41, 74), and the area with the SFCs needs special protection. The present study provides a valuable reference for the primary prevention of landslides.

摘要

滑坡分析防止滑坡对居民安全和财产造成威胁，主要的方法是敏感性评估，既麻烦又费时。提出关联规则算法(ARA)来简单快速地挖掘因子与滑坡之间的相关性。原来的ARA不能反映滑坡的范围，滑坡分析是不可忽略的，因此改进以挖掘频繁的次要因素组合（SFCs）。首先，使用袋外误差和卡方（${\mathrm{\chi }}^2$） 测试。采用滑坡敏感性评估进一步验证了因子选择的准确性，该评估使用随机选择的研究网格数据的 30% 作为训练数据进行预测。改进后的 ARA 利用历史滑坡面积对频繁出现的 SFC 进行挖掘，然后通过频率比和${\mathrm{\chi }}^2$测试。得出的结论是，频繁出现的 SFC 是：(21, 41)、(21, 74)、(34, 41)、(34, 74)、(41, 74)、(21, 41, 74) 和 (34) , 41, 74)，并且有 SFC 的区域需要特殊保护。该研究为滑坡的一级预防提供了有价值的参考。