Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 611756, China; Department of Geography, National University of Singapore, Kent Ridge, 117570, Singapore.
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 611756, China.
J Environ Manage. 2022 Nov 15;322:116108. doi: 10.1016/j.jenvman.2022.116108. Epub 2022 Sep 3.
Landslide is a hazard that has drastic repercussions on population and the environment worldwide. Landslide susceptibility mapping (LSM) is vital for landslide disaster management and formulating mitigation strategies. In this study, with the support of geographic information system and remote sensing, a new LSM hybrid framework is developed based on random forest (RF) and cusp catastrophe model (CCM). Under the framework, 15 conditioning factors and 2082 historical landslides are selected to test and compare its performance in a landslide-prone area in Liangshan, Southwest China. The results depicted a better performance of the new LSM hybrid framework (RF-CCM) than those of RF or traditional application mode of catastrophe model (Catastrophe fuzzy membership functions, CFMFs) only. The RF-CCM achieved the highest accuracy (0.901), the narrowest confidence interval (0.895-0.907), and the smallest standard error (0.004) among all the models. Notably, RF-CCM successfully decreased the uncertainty of CFMFs in determining the relative importance of conditioning factors, overcame the dependence of the CFMFs on independence among the conditioning factors, and had a higher stability level than RF. Moreover, distance to human engineering activities and slope had the greatest impact on LSM in the modeling process. The study result can provide insights for developing reliable predictive models for other landslide-prone areas with similar geo-environmental conditions.
滑坡是一种全球性的灾害，对人口和环境都有巨大的影响。滑坡易发性制图（LSM）对于滑坡灾害管理和制定减灾策略至关重要。本研究在地理信息系统和遥感的支持下，基于随机森林（RF）和尖点突变模型（CCM）开发了一种新的 LSM 混合框架。在该框架下，选择了 15 个条件因素和 2082 个历史滑坡，以测试和比较其在中国西南地区凉山地区易滑坡地区的性能。结果表明，新的 LSM 混合框架（RF-CCM）的性能优于 RF 或传统的突变模型应用模式（突变模糊隶属函数，CFMFs）。RF-CCM 在所有模型中达到了最高的准确性（0.901）、最窄的置信区间（0.895-0.907）和最小的标准误差（0.004）。值得注意的是，RF-CCM 成功地降低了 CFMFs 在确定条件因素相对重要性方面的不确定性，克服了 CFMFs 对条件因素独立性的依赖，并且比 RF 具有更高的稳定性水平。此外，在建模过程中，距人类工程活动和坡度的距离对 LSM 的影响最大。该研究结果可为开发具有类似地质环境条件的其他易滑坡地区的可靠预测模型提供参考。
