基于信息量与神经网络模型的滑坡易发性评价

doi:10.13722/j.cnki.jrme.2019.1094

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Abstract The traditional method of randomly selecting non-slide units has the disadvantage of low accuracy when the evaluation of landslide susceptibility is carried out only based on neural network model. Thus，this paper proposes a susceptibility evaluation model that combines information value with neural network. Taking Shangyou County as a case study area. Firstly，ten environmental factors including slope，elevation，aspect，plane curvature，profile curvature，vegetation index(NDVI)，topographic wetness index(TWI)，distance to stream，distance to road and land use were employed to perform regional landslide susceptibility evaluation according landslide catalog and actual survey in the study area. Secondly，the susceptibility zoning of Shangyou County was carried out with the information value model，and the susceptibility zoning map of Shangyou County was obtained. Then，the non-landslide units were selected from the low-prone area in the susceptibility zone obtained from the information value model，and the test and training sets with the historical landslide points in the landslide catalog were divided. All the grids of the study area were then input into the resulting models to predict the landslide probability. Finally，the natural breakpoint method was used to classify the probability often grids，and a zoning map of landslides susceptibility based on the combination of information value and artificial neural network was obtained. The susceptibility results show that：the area under the success rate curve of the independent information model is AUC = 0.736 4，and the number of historical disaster points located in high-susceptibility areas and higher-susceptibility areas accounts for 55.6% of the total number of disasters；In comparison，the combined model based on both information value and neural network achieved a AUC value of 0.787 4，and the number of disasters located in the high-prone areas accounted for 85.8% of the total disasters. The evaluation accuracy of the information value-neural network is 5.1% higher than that of the information value model，and the number of disasters covered by high-prone areas is 30.2% higher than that of information value model. This implies that the information value-neural network model has better evaluation accuracy. It is verified that selecting non-slide points in extremely low-prone areas derived from the information value model is feasible for landslide susceptibility modelling.

Key words： slope engineering information model neural network landslide susceptibility non-landslide unit

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	Articles by authors
	CHEN Fei1
	2
	CAI Chao1
	LI Xiaoshuang1
	2
	SUNTao1
	2
	QIAN Qian1

Cite this article:

CHEN Fei1,2,CAI Chao1, et al. Evaluation of landslide susceptibility based on information volume and neural network model[J]. , 2020, 39(S1): 2859-2870.

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http://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2019.1094 OR http://rockmech.whrsm.ac.cn/EN/Y2020/V39/IS1/2859

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