College of Construction Engineering, Jilin University, Changchun 130026, China

There are large number of slope geological hazards in Yongji County of Jilin Province. In order to provide an efficient and intuitive analysis model for the prevention and warning of slope geological hazards, Yongji County of Jilin Province is used as a research area. Eleven evaluation factors including elevation, slope, slope di-rection, profile curvature, plane curvature, distance from faults, lithology, distance from rivers, average annual rainfall, terrain humidity index, and vegetation cover index are selected. Neural network model is used to analyze the susceptibility of regional slope geological hazards, followed by the comparison with frequency ratio and support vector machine models. Finally, the accuracy of the model is verified and analyzed using the ROC curve. The suc-cess rates of the neural network, frequency ratio, and support vector machine models are 91.3%, 89.3% and 90.2%, and the prediction rates are 87.3%, 84.3% and 85.6%, respectively. The results show that the neural network model has the highest accuracy and is more suitable for the assessment of slope geological hazards in Yongji County.