## 5 Discussion  
The accuracy of the hazard assessment was determined by the precision of the parameters. To date, regional geological units have been used as the study units for small-scale seismic landslides in some studies [29; 30]. Accurate rock group data are necessary for studying small-region geological hazards, but the rock groups cannot be divided via small-scale geological maps, such as the commonly used 1:50,000 geological maps. A multilevel physical and mechanical parameter system was established on the basis of field investigation data. Considering the data accuracy of the system, data of the rock-soil mass was divided into three levels: the landslide inventory data, data of the loose accumulation layer originating from remote sensing interpretation, and data of the geological units derived from the regional geological maps. With verification of examples, the precision of the parameters gradually decreases from the first level to the third level and reflects the importance of the landslide inventory in hazard assessment, which is closer to the actual conditions. However, whether the division of multilevel parameter groups is a more accurate method for hazard assessment in small areas can be further analyzed in future work. Owing to the limitations of environmental conditions, the effect of groundwater was not considered for the Newmark displacement model (the study area during the season of less or no rainfall). [PERSON] and [PERSON] [10] considered the effect of groundwater to be simplified in dry conditions, [PERSON] et al. [31] found the predicted displacement of landslides to be far less than the actual displacement in the condition of high groundwater. [PERSON] et al. [12] demonstrated that when different simplified Newmark models are compared, accurate input parameters may have a greater influence on the assessment results. Therefore, the prediction of sliding displacement by considering various groundwater levels should be emphasized in future work. The calculation of Newmark displacement involves a large amount of regional data and seismic parameter data that are not easily accessible, making effective performance difficult for predicting earthquake landslide zones. Thus, many scholars have established empirical formulas to simplify the calculation process. As [PERSON] [21] calculated an empirical regression equation by analyzing a large amount of data in comparison with actual landslides, the equation was verified to be highly accurate and to meet the requirements for predicting the cumulative displacement of seismic landslides. [PERSON] et al. [32] corrected the regression parameters and established a Newmark model for the Wenchuan earthquake on the basis of 221 ground motion records of the earthquake. [PERSON] et al. [33] developed new fitting coefficients for the Newmark model on the basis of the dataset of the 2013 Lushan _Ms_ 7.0 earthquake, and in this study, on the basis of prior experience, the empirical formula of Jibson is used to assess seismic landslide hazards. Moreover, [PERSON] and [PERSON] [34] reported that the different empirical formulas for the Newmark model may have little effect on the accuracy of landslide hazard assessment. Therefore, the use of empirical formulas might not affect the accuracy of the assessment, even if not in similar or analogous regions. [PERSON] et al. [35] presented a new method of integrating statistical analysis models (linear regression (LR) and support vector machine (SVM)) with critical acceleration (\(a_{c}\)) for earthquake landslide hazard assessment, and demonstrated that the new assessment method is more effective than the simplified Newmark model for seismic landslide hazards. The information value model is a statistical analysis model that statistically analyzes relevant data to calculate the landslide hazard zone on the basis of historical seismic landslide events. [PERSON] et al. [36] showed that a combined model is a significant improvement over the Newmark displacement model alone.