基于贝叶斯方法的降雨诱发滑坡概率型预警模型研究

doi:10.13722/j.cnki.jrme.2022.0382

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Abstract To improve the early warning accuracy of rainfall-induced landslides，the Qinba Mountains region in southern Shaanxi province is taken as an example. At first，artificial neural network(ANN) and logistic regression model(LR) were used to establish the landslide susceptibility model，and the established susceptibility model was tested and corrected by frequency ratio model(FR) to express the spatial probability of landslide occurrence；Secondly，sensitivity analysis method was employed to select the optimal rainfall variables and the attenuation coefficient K，and then，two-dimensional Bayesian approach was be used to establish probabilistic threshold model，which can be used to calculate the temporal probability of landslide. The model was tested by the rainfall data from 2016 to 2020；Then，the spatial probability and temporal probability of rainfall-induced landslides were coupled base on Bayesian formula，and a probabilistic early warning model for rainfall-induced landslide(PLEWM) was proposed. To test the performance advantages of PLEWM，PLEWM and traditional early warning model were separately used to issue warning information day-by-day for the rainy season(July to September) from 2016 to 2020. It is proposed to use the investment of operating LEWM(Invest)，losses caused by landslides (Loss)，correct alert rate，missed alert rate and false alert rate as warning model performance indicators，to compare the performance differences between the PLEWM and traditional early warning model. The results show that：(1) EE-D is the optimal combination for rainfall threshold model in the study area，and the optimal attenuation coefficient K is 0.816. (2) Probabilistic threshold model predicts that 213.71 triggering rainfalls will occur from 2016 to 2020，and 201 actually recorded，with a cumulative error of 10.07%，the predicted triggering rainfall and the actual recorded in each probability intervals are distributed along a diagonal line with a slope of 1. (3) According to the statistics of the warning information issued in the rainy season from 2016 to 2020，The Invest and Loss of the PLEWM are 62.86% and 63.48% of traditional early warning model，respectively. The correct alert rate，missed alert rate and false alert rate are 63.99%，34.71% and 1.3% respectively，which are better than the traditional warning model；During the condition of long-lasting and high-intensity rainfall，the performance of PLEWM is significantly higher than traditional warning model.

Key words： slope engineering rainfall-induced landslide landslide susceptibility probabilistic rainfall threshold；Bayesian approach probabilistic landslide early warning model

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	Articles by authors
	SONG Yufei1
	CAO Yanbo1
	2
	FAN Wen1
	2
	YU Ningyu1
	ZUO Chen3
	TAO Hong4

Cite this article:

SONG Yufei1,CAO Yanbo1,2, et al. Probabilistic early warning model for rainfall-induced landslides based on Bayesian approach[J]. , 2023, 42(3): 558-574.

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http://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2022.0382 OR http://rockmech.whrsm.ac.cn/EN/Y2023/V42/I3/558

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