山崩
均方误差
统计
理论(学习稳定性)
边坡稳定性
接收机工作特性
数字高程模型
环境科学
数据挖掘
计算机科学
数学
遥感
地质学
岩土工程
机器学习
作者
Jean Baptiste Nsengiyumva,Geping Luo,Egide Hakorimana,Richard Mind'je,Aboubakar Gasirabo,Valentine Mukanyandwi
出处
期刊:Risk Analysis
[Wiley]
日期:2019-11-01
卷期号:39 (11): 2576-2595
被引量:9
摘要
The use of appropriate approaches to produce risk maps is critical in landslide disaster management. The aim of this study was to investigate and compare the stability index mapping (SINMAP) and the spatial multicriteria evaluation (SMCE) models for landslide risk modeling in Rwanda. The SINMAP used the digital elevation model in conjunction with physical soil parameters to determine the factor of safety. The SMCE method used six layers of landslide conditioning factors. In total, 155 past landslide locations were used for training and model validation. The results showed that the SMCE performed better than the SINMAP model. Thus, the receiver operating characteristic and three statistical estimators-accuracy, precision, and the root mean square error (RMSE)-were used to validate and compare the predictive capabilities of the two models. Therefore, the area under the curve (AUC) values were 0.883 and 0.798, respectively, for the SMCE and SINMAP. In addition, the SMCE model produced the highest accuracy and precision values of 0.770 and 0.734, respectively. For the RMSE values, the SMCE produced better prediction than SINMAP (0.332 and 0.398, respectively). The overall comparison of results confirmed that both SINMAP and SMCE models are promising approaches for landslide risk prediction in central-east Africa.
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