Faculty of Geology, University of Science, VNU-HCM, Ho Chi Minh City 700000, Vietnam.
Department of Earth Sciences, National Cheng Kung University, Tainan 701, Taiwan.
Sensors (Basel). 2019 Jan 26;19(3):505. doi: 10.3390/s19030505.
This paper proposes a new approach of using the analytic hierarchy process (AHP), in which the AHP was combined with bivariate analysis and correlation statistics to evaluate the importance of the pairwise comparison. Instead of summarizing expert experience statistics to establish a scale, we then analyze the correlation between the properties of the related factors with the actual landslide data in the study area. In addition, correlation and dependence statistics are also used to analyze correlation coefficients of preparatory factors. The product of this research is a landslide susceptibility map (LSM) generated by five factors (slope, aspect, drainage density, lithology, and land-use) and pre-event landslides (Typhoon Kalmaegi events), and then validated by post-event landslides and new landslides occurring in during the events (Typhoon Kalmaegi and Typhoon Morakot). Validating the results by the binary classification method showed that the model has reasonable accuracy, such as 81.22% accurate interpretation for post-event landslides (Typhoon Kalmaegi), and 70.71% exact predictions for new landslides occurring during Typhoon Kalmaegi.
本文提出了一种新的方法,即使用层次分析法(AHP),将 AHP 与二元分析和相关统计相结合,以评估两两比较的重要性。我们不是通过总结专家经验统计来建立一个量表,而是分析研究区域中相关因素的特性与实际滑坡数据之间的相关性。此外,还使用相关和依赖统计来分析预备因素的相关系数。这项研究的成果是一张由五个因素(坡度、方位、排水密度、岩性和土地利用)和前事件滑坡(台风“格美”事件)生成的滑坡敏感性图(LSM),然后通过后事件滑坡和事件期间发生的新滑坡(台风“格美”和台风“莫拉克”)进行验证。通过二元分类方法验证结果表明,该模型具有合理的准确性,例如对后事件滑坡(台风“格美”)的准确解释为 81.22%,对台风“格美”期间发生的新滑坡的准确预测为 70.71%。