Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method
Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method
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DOI：
10.1007/s11069-020-04452-4
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发表时间：
2021-01
期刊：
影响因子：
3.7
通讯作者：
Xinfu Xing;Chenglong Wu;Jinhui Li;Xueyou Li;Limin Zhang;R. He
中科院分区：
文献类型：
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作者：
Xinfu Xing;Chenglong Wu;Jinhui Li;Xueyou Li;Limin Zhang;R. He
滑坡敏感性是指一个地区发生滑坡的可能性。逻辑回归（LR）方法是滑坡易发性评价中最常用的方法之一。对于滑坡诱发的滑坡，一般采用年或月降雨量作为滑坡敏感性的LR模型。该模型是一个静态敏感性模型，限制了其在未来降雨条件下滑坡概率预测的应用。本研究提出一种修正的逻辑回归方法，以实现累积日降雨量下的动态滑坡易发性预测。在滑坡敏感性评价中，采用了5种累积日降雨量。最新的滑坡事件被用来更新滑坡敏感性模型。利用受试者工作特征曲线和曲线下面积评价预测的可靠性。以深圳市滑坡敏感性评价为例，说明了该方法的应用.结果表明，利用过去10年的7次极端降雨事件更新滑坡易感性模型，该方法的准确率达到91.9%。该方法利用未来降雨量预报，对大范围的潜在地质灾害进行了超前预报。
Landslide susceptibility is the likelihood of a landslide occurring in an area. The logistic regression (LR) method is one of the most popular methods for landslide susceptibility assessment. For rainfall-induced landslides, yearly or monthly rainfall is commonly used to establish a landslide susceptibility model by the LR method. It is a static susceptibility model, which limits the application to predict future landslide probability under potential rainfall event. This study presents a revised logistic regression method to achieve dynamic landslide susceptibility prediction under cumulative daily rainfall. Five kinds of cumulative daily rainfall are used in the landslide susceptibility assessment. The latest landslide events are used to update the landslide susceptibility model. The receiver operation characteristic curve and area under curve are utilized to evaluate the prediction reliability. The landslide susceptibility assessment in Shenzhen is taken as an illustration of the proposed method. The result indicates the method is capable to achieve a high accuracy of 91.9% when the landslide susceptibility model is updated using seven extreme rainfall events in the past 10 years. This method provides an advance prediction of the potential geo-hazards for a large area using the future rainfall forecast.