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Landslide susceptibility assessment based on different machine-learning methods in Zhaoping County of eastern Guangxi
Natural Hazards and Earth System Sciences ( IF 4.6 ) Pub Date : 2020-08-27 , DOI: 10.5194/nhess-2020-251
Chunfang Kong , Kai Xu , Junzuo Wang , Chonglong Wu , Gang Liu

Abstract. Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study were implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility by Zhaoping County. To this end, 10 landslide disaster-related causal variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were selected for running four machine-learning (ML) methods, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, and field investigation were performed to verify the efficiency of these models. Analysis and comparison of the results denoted that all four ML models performed well for the landslide susceptibility evaluation as indicated by the values of ROC curves – from 0.863 to 0.934. Moreover, the results also indicated that the PSO algorithm has a good effect on SVM and FR models. In addition, such a result also revealed that the PSO-RF and PSO-SVM models have the strong robustness and stable performance, and those two models are promising methods that could be transferred to other regions for landslide susceptibility evaluation.

中文翻译:

基于机器学习方法的桂东昭平县滑坡敏感性评价

摘要。关于广西东部严重滑坡灾害的频发和频繁发生,目前的研究是采用支持向量机(SVM),粒子群优化支持向量机(PSO-SVM),随机森林(RF)和粒子群优化随机森林(PSO-RF)方法来评估昭平县的滑坡敏感性。为此,选择了10种与滑坡灾害相关的因果变量,包括数字高程模型(DEM),气象学,Landsat8,地质学和人类活动因子,以运行四种机器学习(ML)方法,并制作了滑坡敏感性评价图。然后,进行了接收机工作特性(ROC)曲线和现场调查,以验证这些模型的效率。结果的分析和比较表明,所有四个ML模型在滑坡敏感性评估中都表现良好,如ROC曲线的值所表明的(从0.863到0.934)。此外,结果还表明,PSO算法对SVM和FR模型具有良好的效果。此外,这样的结果还表明,PSO-RF和PSO-SVM模型具有很强的鲁棒性和稳定的性能,这两个模型是有希望的方法,可以转移到其他地区进行滑坡敏感性评估。
更新日期:2020-08-27
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