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.

滑坡易感性是某个地区发生滑坡的可能性。对数回归（LR）方法是滑坡敏感性评估中最受欢迎的方法之一。对于降雨诱发的滑坡，通常采用年降水量或月降水量通过LR方法建立滑坡敏感性模型。这是一个静态敏感性模型，它限制了在潜在降雨事件下预测未来滑坡概率的应用。这项研究提出了一种修正的逻辑回归方法，以在累积日降雨量下实现动态滑坡敏感性预测。滑坡敏感性评估中使用了五种累积日降水量。最新的滑坡事件用于更新滑坡敏感性模型。利用接收机工作特性曲线和曲线下面积来评估预测可靠性。本文以深圳的滑坡敏感性评价为例。结果表明，该方法在过去10年中使用7次极端降雨事件更新滑坡敏感性模型时，能够达到91.9％的高精度。这种方法可以使用未来的降雨预报来对大面积的潜在地质灾害进行提前预测。在过去10年中使用7次极端降雨事件更新滑坡敏感性模型时，为9％。这种方法可以使用未来的降雨预报来对大面积的潜在地质灾害进行提前预测。在过去10年中使用7次极端降雨事件更新滑坡敏感性模型时，为9％。这种方法可以使用未来的降雨预报来对大面积的潜在地质灾害进行提前预测。