## 4 Uncertainty Analysis
### Model Selection and Application
#### 4.2.1 Traditional Machine Learning Models  
[PERSON] et al. [11] evaluated three models, namely, the frequency ratio (FR), LR, and ANN, for landslide susceptibility assessment. They demonstrated that ANNs outperformed traditional statistical models in terms of accuracy. The ANN model aims to simulate how the human brain processes information, enabling the model to learn the complex relationships between input and output variables. This model is considered an appropriate machine learning method for predicting nonlinear and complex phenomena. Consequently, artificial neural network (ANN) models are among the best techniques for accurately predicting landslides. [PERSON] et al. [80] used the ANN approach to create a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia. The study employed a landslide inventory map containing 140 landslide locations, which were randomly divided into training and testing sets in a 70:30 ratio. The authors selected nine landslide-influencing factors as model inputs: altitude, elevation, slope, aspect, curvature, topographic wetness index (TWI), distance to roads, distance to rivers, lithology, and rainfall. To validate the effectiveness of the landslide prediction model, the authors used the area under the curve (AUC) and several statistical metrics (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value). The results showed that the ANN prediction model achieved excellent performance in validation, with AUC values of 0.940 for both the training and testing sets. [PERSON] et al. [104] conducted a landslide susceptibility mapping (LSM) study in Zanjan, Iran, using geographic information system (GIS) technology to identify the most critical factors contributing to landslides. The study compared convolutional neural networks (CNNs) with four machine learning (ML) algorithms, including random forest (RF), an artificial neural network (ANN), a support vector machine (SVM), and logistic regression (LR). The authors extracted 16 causative factors for landslides and prepared the corresponding spatial layers, then used these algorithms to train on landslide and non-landslide points. The results indicated that all five machine learning algorithms performed well, with accuracies ranging from 82.43% to 85.6% and AUC values from 0.934 to 0.967. Among these, the random forest algorithm achieved the best results, followed by CNN, SVM, ANN, and LR. The variable importance analysis revealed that slope and terrain curvature contributed the most to landslide prediction. These findings are significant for developing landslide risk management strategies. [PERSON] et al. [105] combined geographic information system (GIS) technology with five machine learning algorithms--logistic regression (LR), the support vector machine (SVM), random forest (RF), a gradient boosting machine (GBM), and multilayer perceptron (MLP)--to assess landslide susceptibility in Shexian County, China. The landslide susceptibility results of the five methods were compared using an area under the curve (AUC) analysis and grid maps. The results showed that the proportions of high or very high landslide points for LR, SVM, RF, GBM, and MLP were 1.52, 1.77, 1.95, 1.83, and 1.64, respectively. The ratios of very high landslide points to graded areas were 1.92, 2.20, 2.98, 2.62, and 2.14, respectively. The success rates of the training samples for the five methods were 0.781, 0.824, 0.853, 0.828, and 0.811, with prediction accuracies of 0.772, 0.803, 0.821, 0.815, and 0.803, respectively. The accuracy ranking of the five algorithms was RF > SVM > MLP > GBM > LR. The study results indicate that all five machine learning algorithms performed well in assessing landslide susceptibility in Shexian County, with the random forest algorithm achieving the best results. Most studies have shown that RF exhibits higher predictive accuracy compared to other models such as LR, SVM, and ANN, making it more suitable for landslide susceptibility mapping [17; 106].