## 5 Conclusions

Table 3: Evaluation indicators for quantitative screening. indexes and subjective dependence that exists in the traditional method. The main research results are as follows:  
1. Multi-dimensional indicator screening framework: A qualitative analysis framework was constructed based on engineering geology, hydrogeology, and anthropogenic factors, and the preliminary selection of 60 indicators was quantitatively screened by combining it with the improved grey correlation model, and 10 core indicators were finally identified, including rock structure, slope height, groundwater body, internal friction angle, cohesion, maximum monthly rainfall, and geological structure. The framework achieves the precision and efficiency of the indicator system through a hierarchical screening mechanism, providing a scientific basis for the evaluation process. 2. Evaluation system validity and reliability test: By calculating the validity coefficient (\(\beta=0.0816\)) and reliability coefficient (\(\rho=0.9609\)), the logical rationality and data consistency of the screening indicator set were verified. The results show that the method takes into account expert experience and objective data analysis, significantly reduces human bias, and can provide a highly credible indicator basis for slope stability evaluation. 3. Engineering practice value: The research results provide a standardized analysis tool for loess slope stability assessment, which can systematically identify potential risk factors, guide the deployment of the monitoring network, optimize the reinforcement scheme, and help improve the refinement of mine safety management. The method also has cross-regional adaptability and can be extended to the evaluation of loess slopes with different geological backgrounds. Loess slopes in the southern Jingshan region of Lingtai County, Gansu Province, are the subject of this study. The model's broad applicability needs more validation due to the limited expert samples and data coverage, especially across varied geological units including steep high slopes and composite stratigraphy. The proposed indicator screening model could be expanded in future research through the integration of critical parameters including extreme rainfall measurements, seismic indices, and freeze-thaw cycles into existing evaluation systems while adopting machine learning techniques to quantify coupling effects among these factors. Additionally, comprehensive model validation and numerical simulations across various geomorphological units of the Loess Plateau could elucidate the evolution mechanisms of slope collapse under extreme loading conditions. These enhancements would substantially improve the model's adaptability to complex scenarios involving intense rainfall and seismic activities in loess terrains while strengthening its threshold identification capability. Conceptualization, H.S. and J.A. ; Writing--original draft, J.L. All authors have read and agreed to the published version of the manuscript. 2024 Liaoning Provincial Science and Technology Plan Joint Programme Project [2024011314-JH2/1026]. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Author [PERSON] was employed by the company Jinzhou Water (Group) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.