基于水土耦合物理机制的区域滑坡概率预报技术研究

Research on Physics-based Probabilistic Forecasting Technology of Regional Landslide

基于水土耦合物理机制的滑坡预报是通过计算安全系数给出确定性的预报结果,其可靠性依赖模型输入数据的精度,尤其是土体粘结力和内摩擦角。但这些数据尤其在区域尺度上存在较大的不确定性,进而导致安全系数存在不确定性。为解决该问题,引入了蒙特-卡洛随机取值的方法,提出了基于水土耦合物理机制的滑坡概率预报模型,并首次尝试选取西南地区为试验区域开展滑坡预报试验及检验。结果表明:2017年汛期发生了11次典型的滑坡案例,该模型成功预报9次,成功率达82%。具有较好的预报准确率,可为地质灾害防灾减灾提供技术支撑。

Conventional outputs of physics-based landslide forecasting models are presented as deterministic warnings through calculating the safety factor (Fs) of slopes. However, these models are highly dependent on variables such as cohesion force and internal friction angle which are affected by a high degree of uncertainty especially at a regional scale, resulting in unacceptable uncertainties of F s. To address this issue, Monte Carlo method is adopted and the physics-based probabilistic forecasting model was proposed here, based on which, The model is implemented and evaluated in southwest of China. The testing results show that the model successfully forecasted 9 landslides cases from the 11 total testing cases during 2017, the successful rate reaches 82%,and has a high accuracy of prediction and strong applicability. So it is expected to provide the technology supports for landslide mitigations.