基于预警隶属度函数多模型融合的滑坡预警方法

不同的滑坡预报模型存在预报同一滑坡可能提前也有可能延迟、预报精度差异较大的问题,而且,融合多模型预警方法未能充分体现个体模型预报特征,其融合预警精度不高。在分析模型可靠性的基础上,区分预报提前和延迟2种情况,设计不同的发生滑坡隶属度函数,以最低风险原则确定滑坡预警隶属度函数,利用模糊积分实现多模型融合滑坡预警。利用16个已知滑坡对预报模型进行可靠性评价,另选4个已知滑坡分别进行滑坡发生3d前和1d前多模型融合预警验证实验,结果表明,多模型融合预警虚警率比多个单一模型平均虚警率分别降低16.6%和25.0%。实验表明利用预警隶属函数进行多模型滑坡预警能提高预警精度20.0%左右。

Multi-Model Fusion Method for Landslide Early Warning Based on Early Warning Membership Function

Different landslide prediction models have the problem of predicting the same landslide in advance or delay, and the prediction accuracy is quite different. At present, the fusion multi-model early-warning method fails to fully reflect the characteristics of individual model prediction, and the fusion early-warning accuracy is not high. On the basis of analyzing the reliability of these models, two landslide occurrence membership functions of delay and advance were designed respectively. According to the principle of minimum risk, the membership function of landslide warning was determined, and multi-model fusion of landslide early warning was realized by fuzzy integral. Sixteen known landslides were used to evaluate the reliability of the prediction models, and the other four known landslides were used to carry out the fusion early warning experiment of the landslide three days before and one day before the landslide occurrence, and the false alarm rate of multi-model fusion early warning was reduced by 16.6% and 25% respectively, compared with the average false alarm rate of multiple single models. The experiment shows that using early warning membership function for multi-model landslide early warning can improve the early warning accuracy for about 20%.