# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 6 Conclusions  
In this study, using Mei County as our study area, we screened and identified 16 landslide conditioning factors in conjunction with 87 landslides and analyzed their relationships with the landslides. We also explored the effectiveness of four machine learning models, namely, Multi-Layer Perceptron, Naive Bayes, Credible Decision Trees, and Random Forests, in assessing landslide susceptibility, selected the optimal parameters for computation, and mapped the landslide susceptibility based on the computed LSI values. The main conclusions are as follows:  
1. Among the many influencing factors, the slope orientation, distance from roads, and land use type are strongly associated with the occurrence of landslides in the study area. This is likely due to geomorphological controls on slope stability and the impact of human activities near road networks. In contrast, the STI, SPI, TWI, and NDVI showed weaker correlations, possibly due to low variability or indirect influence on slope failure mechanisms.
2. Four partitioning methods were used to partition the results for each of the four model calculations, and the best results were obtained by using the geometric interval model. This method outperformed others because it effectively handled skewed distributions and enhanced the spatial contrast between susceptibility levels, aligning well with actual landslide occurrences.
3. The CDT model displayed significant segmentation issues in its landslide susceptibility analysis, leading to overly generalized classification results and lower accuracy. This probably stems from its conservative splitting mechanism, which underrepresents spatial heterogeneity. The NB model also showed limitations in its predictive performance, possibly due to its strong independence assumptions among features which rarely hold true in complex terrain. In contrast, the MLP and RF models demonstrated higher reliability and accuracy, making them better suited for landslide susceptibility assessments.  
Based on the LSI distribution, the RF model exhibited the best overall performance, providing well-defined susceptibility zones, due to its ensemble learning structure and robustness to noise. The MLP model effectively highlighted areas of very low and very high landslide susceptibility, offering a more detailed delineation of landslide-prone regions, benefiting from its flexible architecture. The models, ranked from best to worst in terms of performance, are as follows: RF, MLP, NB, and CDT.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15116325/s1](https://www.mdpi.com/article/10.3390/app15116325/s1)]([https://www.mdpi.com/article/10.3390/app15116325/s1](https://www.mdpi.com/article/10.3390/app15116325/s1)), Table S1. CF values for different conditioning factors.  
Conceptualization, W.C. and C.Z.; methodology, W.C., C.Z. and D.L.; software, C.Z. and D.L.; validation, W.C., P.T. and I.I.; formal analysis, W.C., D.L., P.T. and I.I.; investigation, W.C., C.Z., D.L. and P.T.; writing--original draft preparation, W.C., S.M., C.Z., D.L., P.T. and I.I.I.; writing--review and editing, W.C., S.M., C.Z., P.T. and I.I.I. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Innovation Capability Support Program of Shaanxi (Program No. 2020 KJX-005).  
Not applicable.  
The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Kunming Coal Design and Research Institute Co., Ltd. Author [PERSON] was employed by the company Yunnan Xiaolongtan Mining Bureau 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.