# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 5 Discussion
Most of the recorded landslides were concentrated in forest park regions, such as Taibai Mountain and Red River Valley, as well as surrounding mountainous areas, including exposed loess cropland, semi-exposed \"V\"-shaped gullies, and regions along the Tangyu River watershed, particularly in Qizhen, Hengqu, and Yingtou. The landslide susceptibility maps generated by the MLP, NB, and RF models closely aligned with the actual landslide distribution, whereas the CDT model, when classified using the quantile and natural break methods, produced results that significantly deviated from the other models. A comparison of the landslide density maps derived from the remaining two classification methods confirmed that the geometric interval method produced the most reliable and accurate zoning results. This could be attributed to its ability to accommodate skewed LSI distributions and enhance the spatial distinction between susceptibility classes, leading to more realistic zoning patterns that better reflected observed landslide occurrences. This is not always the case, as mentioned by other researchers that prefer other classification schemes, mostly Natural Breaks [17]. However, the observed consistency of higher accuracy among the MLP, NB, and RF models in our case underscores the importance of choosing an appropriate classification method. Overall, the MLP achieved the most pronounced delineation of the study area, with AUC values of 0.746 and 0.868 for the MLP and RF models in the training dataset and 0.806 and 0.751 in the validation dataset, respectively. Compared with the NB and CDT models, the accuracies of their calculation results were better. Meanwhile, in the validation dataset, the 95% CIs of the MLP and RF model were 0.673-0.903 and 0.612-0.861, respectively, with a more concentrated distribution. However, the MLP model's P-value was relatively small for both the training and validation samples. Since it is the most complex of the four models, combining the selected parameters, specific noise or details are recognized during the learning process, and the MLP model may have overfitted. The optimal iteration parameter chosen for the RF model was not too high, which would make the number of trees smaller, the depth of each tree correspondingly larger, and the complexity higher, while the \(p\)-value was not small enough to run the risk of underfitting. Similarly, the training dataset was larger than the validation dataset, meaning that the complexity of the model needed to be higher to compute the training dataset than the complexity of the validation dataset, and the corresponding risk of overfitting was larger. This meant that the \(p\)-value in the validation dataset of the four models was a little larger. Combining the selected model parameters and the computational results of the model, the RF model is more suitable for landslide susceptibility assessment in this area, and the MLP model is more prone to overfitting problems. The CDT and NB models are relatively ineffective in the assessment of landslide susceptibility. The observed differences in model performance likely stem from their underlying learning architectures and assumptions. The MLP model, while highly flexible in capturing nonlinear relationships, is particularly sensitive to noise and parameter settings, which may explain its tendency toward overfitting--especially when trained on a relatively small and balanced dataset. As [PERSON] et al. [19] noted, MLP does not require pre-assumptions about data distribution, and it adjusts input-variable weights dynamically during training, enabling greater adaptability to complex spatial patterns. The use of the back-propagation algorithm further minimizes errors between actual and predicted outcomes, enhancing predictive precision. These attributes have enabled MLP to achieve high accuracy in previous LSM studies [19; 79; 80], a trend also supported by the present results, where it achieved the highest PR and SR values (86.68% and 90.08%, respectively).